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sensors
Article
A Soft Tactile Sensor Based on Magnetics and Hybrid
Flexible-Rigid Electronics
Miguel Neto 1, 2, *, Pedro Ribeiro 1,2,3, Ricardo Nunes 2,4, Lorenzo Jamone 3, Alexandre Bernardino 2,4
and Susana Cardoso 1,2
Citation: Neto, M.; Ribeiro, P.;
Nunes, R.; Jamone, L.; Bernardino, A.;
Cardoso, S. A Soft Tactile Sensor
Based on Magnetics and Hybrid
Flexible-Rigid Electronics. Sensors
2021,21, 5098. https://doi.org/
10.3390/s21155098
Academic Editors: Rezia Molfino and
Francesco Cepolina
Received: 22 June 2021
Accepted: 20 July 2021
Published: 28 July 2021
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1Instituto de Engenharia de Sistemas e Computadores—Microsistemas e Nanotecnologias,
1000-019 Lisbon, Portugal; pribeiro@inesc-mn.pt (P.R.); scardoso@inesc-mn.pt (S.C.)
2Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal;
rnunes@isr.tecnico.ulisboa.pt (R.N.); alex@isr.tecnico.ulisboa.pt (A.B.)
3Advanced Robotics at Queen Mary, School of Electronic Engineering and Computer Science,
Queen Mary University of London, London E1 4NS, UK; l.jamone@qmul.ac.uk
4Institute for Systems and Robotics (ISR), 1049-001 Lisbon, Portugal
*Correspondence: mneto@inesc-mn.pt; Tel.: +351-213100237
Abstract:
Tactile sensing is crucial for robots to manipulate objects successfully. However, integrating
tactile sensors into robotic hands is still challenging, mainly due to the need to cover small multi-
curved surfaces with several components that must be miniaturized. In this paper, we report the
design of a novel magnetic-based tactile sensor to be integrated into the robotic hand of the humanoid
robot Vizzy. We designed and fabricated a flexible 4
×
2 matrix of Si chips of magnetoresistive spin
valve sensors that, coupled with a single small magnet, can measure contact forces from 0.1 to 5 N on
multiple locations over the surface of a robotic fingertip; this design is innovative with respect to
previous works in the literature, and it is made possible by careful engineering and miniaturization
of the custom-made electronic components that we employ. In addition, we characterize the behavior
of the sensor through a COMSOL simulation, which can be used to generate optimized designs for
sensors with different geometries.
Keywords: flexible hybrid electronics; magnetic tactile sensor
1. Introduction
Object manipulation is an essential skill for robots to interact with the external world,
especially in environments designed for human beings [
1
]. It is not a trivial task because it
requires the successful coordination of body parts (i.e., head and eyes, arm, hand, fingers)
and sensory channels (i.e., vision, touch, proprioception).
One crucial component in object manipulation, but still not fully developed in robotic
manipulation, is tactile sensing [
1
,
2
]. In fact, although tactile sensing has been an active
research area for more than three decades [
3
], the effective integration of tactile sensing sys-
tems into robots is still limited [
4
], even for robotic hands [
5
]. One of the biggest challenges
is to cover small multi-curved surfaces, such as the fingertip of a robot hand, with several
sensors so that the contact forces can be measured on multiple locations simultaneously.
This challenge can be tackled by using miniaturized components and flexible electronics,
in addition to clever design solutions that would minimize the number of components
without sacrificing the quality of the measurements. In the rest of the manuscript, we report
the design and realization of a physical sensor that has been successfully integrated in the
finger of a humanoid robot (i.e., Vizzy [
6
]), in addition to the simulations that characterize
the fabricated tactile sensor. Our main contributions are: a novel design for magnetic-based
tactile sensors and a physical realization of a miniaturized device.
The rest of the paper is organized as follows: In Section 2we discuss the most relevant
related works, and we outline our main contributions. In Section 3we describe the materials
Sensors 2021,21, 5098. https://doi.org/10.3390/s21155098 https://www.mdpi.com/journal/sensors
Sensors 2021,21, 5098 2 of 23
and methods used in our research. In Section 4we report on an extensive set of experiments
aimed at characterizing the behavior of the sensor, both in simulation and with a real-world
prototype. In Section 5we conclude the paper by summarizing our main findings and
discussing the most promising extensions to this work.
2. Related Work and Our Contribution to the State of the Art
2.1. Transduction Methods and Sensor Design
Tactile sensing requires transforming a mechanical deformation into an electronic
signal. To achieve this, several transduction methods have been proposed [
7
]: e.g., capac-
itive [
8
], piezoresistive [
9
], piezoelectric [
10
], optical [
11
–
13
], and magnetic [
14
,
15
]. The
magnetic tactile sensors usually benefit from high robustness, low or inexistent mechanical
hysteresis, low cost, and are relatively easy to assemble [16].
Ideally, it would be desirable for a sensor to show: high sensitivity, high signal-to-noise
ratio, low hysteresis, and high spatial and temporal resolution [
17
]. All these characteristics
must be maintained when the sensor is mechanically and electronically integrated within
the target robotic system.
Each transduction method has some benefits and some drawbacks, and the best choice
often depends on the application. For example: capacitive sensors often show a low signal-
to-noise ratio, piezoresistive sensors suffer from hysteresis, piezoelectric sensors offer low
spatial resolution, and optical sensors are difficult to miniaturize [
7
]. With respect to other
solutions, magnetic tactile sensors can show very high sensitivity [
14
,
18
], that might come
at the cost of a smaller range of force measurements [
19
]; in addition, they benefit from
high signal-to-noise ratio, and low mechanical hysteresis [
20
]. Notably, low-cost versions
of such sensors, which are also easy to assemble, can be produced as well [16].
Two main working principles for magnetic tactile sensors have been proposed in the
literature: magnetic field [
21
] and electromagnetic induction [
22
]. The most common is
the magnetic field approach, mainly because the sensors of that kind usually have lower
power consumption and are not susceptible to stray capacitance issues as opposed to those
based on electromagnetic induction.
The magnetic field approach requires a magnetic sensor fixed onto the robot finger
and a permanent magnet (PM) embedded into an elastomeric part [
14
] (see Figure 1a).
Applying a force on the elastomeric part changes the relative orientation between the
sensor and the permanent magnet, resulting in a change in the sensor output. However,
this approach assumes knowledge of the point and area of contact (which are often not
known in robotic applications outside structured environments), or that the area of contact
is larger than the whole surface of the sensor; instead, we would like a sensor that could
automatically measure the contact area and location, in addition to the contact force, as
these are essential requirements for grasping activities [
23
]. To overcome this limitation,
we propose a solution where a cylindrical neodymium permanent magnet (1
×
1 mm)
(2) is fixed on the robot finger (4) while a flexible sensor matrix (FSM) ((1) on Figure 1b) is
positioned at the surface of the silicone (3). To the best of our knowledge, this design is
novel, and it thus makes an original contribution to the state of the art.
2.2. Device Manufacturing and Miniaturization
Integration of magnetoelectronic devices in flexible substrates [
24
,
25
] does not match
the performance of spintronic devices in rigid substrates, and these approaches include
unreproducible processes not compatible with small dimension devices [
26
]. The impact
of film strain, bending, and thermal stress on the sensor transport curves have shown to
have significant contributions for films deposited on Si [
27
], polyimide [
28
], or stretchable
substrates [
24
]. To reduce the complexity introduced by mechanical stresses on these
devices, we use a hybrid flexible-rigid approach, where the sensors are processed in silicon
wafers and connected using a flexible printed circuit (FPC) board.
Sensors 2021,21, 5098 3 of 23
Sensors 2021, 21, x FOR PEER REVIEW 3 of 25
substrates [24]. To reduce the complexity introduced by mechanical stresses on these de-
vices, we use a hybrid flexible-rigid approach, where the sensors are processed in silicon
wafers and connected using a flexible printed circuit (FPC) board.
In our magnetic tactile sensor, eight Si rigid chips are arranged in a 4 × 2 matrix cov-
ering the surface area of the elastomeric part. Each Si chip comprises six spin-valve sensors
connected in series and two contact pads to connect to the FPC (Figure 1b,c). Because the
Si chips are rigid, the surface is assumed to be locally flat, enabling simplified models to
evaluate the tactile sensor performance during characterization and simulations. Overall,
the sensor has a total of 8 chips within a surface area of 1.77 cm
2
, which is equal to a spatial
resolution of about 4.5 taxels/cm
2
, which is state of the art for this kind of sensor: therefore,
we consider the manufacturing details of our electronic device as an important contribu-
tion of this work.
Figure 1. Schematic view of the devices’ working principle: (a) concept currently integrated in Vizzy hand [16]; (b) the
flexible sensing matrix concept. The numbers in the figure stand for: 1—sensors (blue); 2—cylindrical Nd permanent mag-
net; 3—polymeric silicone part; 4—robotic finger; 5—air gap; (- - -)—magnetic field lines for the permanent magnet. (c) 3D
view of the FPC with the Si chips embedded in the elastomer part.
3. Materials and Methods
3.1. Device Manufacturing
The tactile sensor’s design is compatible with the Vizzy humanoid robot hands (Fig-
ure 2a) to be located at one of the phalanges, together with reliable electronic components
(Figure 2b,c). This work describes the fabrication of several parts required for the tactile
solution’s success: a rigid chip with microfabricated magnetic sensors, a flexible cable for
contact interconnection, and an elastomer cap layer embedding the sensors. The perma-
nent magnet is attached to the finger’s rigid frame, therefore not moving during the tactile
actuation.
The sensing matrix consists of a matrix of 4 × 2 rigid silicon chips (Figure 2d), each
containing six microfabricated magnetoresistive spin valve sensors [29] (Figure 2f), in a
flexible printed circuit board (FPC) and embedded in a silicone cap, all fabricated at IN-
ESC-MN.
Thanks to the miniaturization of our spin valve sensors, we can fit eight chips in this
design; this creates a tactile sensor area that has a density of sensitive elements (i.e., taxels)
larger than any other magnetic tactile sensor to date. The state-of-the-art solution for mag-
netic flexible skin, uSkin [30], has 2.3 sensors/cm
2
(16 sensors/2.7 × 2.6 cm
2
) [30], while our
proposed solution has 4.5 sensors/cm
2
(8 sensors/1.77 cm
2
), which is more than double.
After fabrication, we followed up with electronics and integration, testing and finite
element simulation. We identify practical challenges regarding each phase and provide
feedback on future iterations as well as for the development of new tactile sensors.
Figure 1.
Schematic view of the devices’ working principle: (
a
) concept currently integrated in Vizzy hand [
16
]; (
b
) the
flexible sensing matrix concept. The numbers in the figure stand for: 1—sensors (blue); 2—cylindrical Nd permanent
magnet; 3—polymeric silicone part; 4—robotic finger; 5—air gap; (
---
)—magnetic field lines for the permanent magnet.
(c) 3D view of the FPC with the Si chips embedded in the elastomer part.
In our magnetic tactile sensor, eight Si rigid chips are arranged in a 4
×
2 matrix
covering the surface area of the elastomeric part. Each Si chip comprises six spin-valve
sensors connected in series and two contact pads to connect to the FPC (Figure 1b,c).
Because the Si chips are rigid, the surface is assumed to be locally flat, enabling simplified
models to evaluate the tactile sensor performance during characterization and simulations.
Overall, the sensor has a total of 8 chips within a surface area of 1.77 cm
2
, which is equal to
a spatial resolution of about 4.5 taxels/cm
2
, which is state of the art for this kind of sensor:
therefore, we consider the manufacturing details of our electronic device as an important
contribution of this work.
3. Materials and Methods
3.1. Device Manufacturing
The tactile sensor’s design is compatible with the Vizzy humanoid robot hands
(
Figure 2a
) to be located at one of the phalanges, together with reliable electronic compo-
nents (Figure 2b,c). This work describes the fabrication of several parts required for the
tactile solution’s success: a rigid chip with microfabricated magnetic sensors, a flexible
cable for contact interconnection, and an elastomer cap layer embedding the sensors. The
permanent magnet is attached to the finger’s rigid frame, therefore not moving during the
tactile actuation.
The sensing matrix consists of a matrix of 4
×
2 rigid silicon chips (Figure 2d), each
containing six microfabricated magnetoresistive spin valve sensors [
29
] (Figure 2f), in
a flexible printed circuit board (FPC) and embedded in a silicone cap, all fabricated at
INESC-MN.
Thanks to the miniaturization of our spin valve sensors, we can fit eight chips in
this design; this creates a tactile sensor area that has a density of sensitive elements (i.e.,
taxels) larger than any other magnetic tactile sensor to date. The state-of-the-art solution
for magnetic flexible skin, uSkin [
30
], has 2.3 sensors/cm
2
(16 sensors/2.7
×
2.6 cm
2
) [
30
],
while our proposed solution has 4.5 sensors/cm
2
(8 sensors/1.77 cm
2
), which is more
than double.
After fabrication, we followed up with electronics and integration, testing and finite
element simulation. We identify practical challenges regarding each phase and provide
feedback on future iterations as well as for the development of new tactile sensors.
Sensors 2021,21, 5098 4 of 23
Sensors 2021, 21, x FOR PEER REVIEW 4 of 25
Figure 2. The robot, the finger, the sensor, the sensing elements, and their integration. (a) Vizzy; (b) a finger from Vizzy
with three phalanges, each with a tactile sensor; (c) redesigned middle phalange part to integrate (d) the electronic inter-
face and (e) flexible hybrid device with the flexible sensing matrix and the (f) Si chips with the spin-valve sensors.
3.2. Rigid Chips with the Magnetoresistive Sensors
The magnetic sensor elements are fabricated in large wafers using industrial pro-
cessing tools at INESC-MN, individualized using a DAD 321 dicing system to their final
dimensions of 0.8 × 1.5 mm
2
. After dicing, the sensors are characterized individually and
selected, as a quality control measure, to secure the device performance across the eight
chips in the sensing matrix. This process versatility is not available in printing or many
other flexible technologies, relying on all the sensors to be viable in a matrix. The geometry
for the sensors produced is shown in Figure 3.
The sensing element consists of a top-pinned spin-valve sensor microfabricated on
top of a Si wafer, with the following stack (thickness in nm): Si/SiO
2
100/Ta 1/NiFe
2.8/CoFe 2.5/Cu 2.6/CoFe 2.3/MnIr 18/Ta 3 deposited by ion beam sputtering in a Nordiko
3000 tool [29]. Notice that both sides of the wafer are coated with the same SiO
2
thermal
oxide to minimize leakage currents through the final device’s substrate.
To obtain a linear response from the sensor, the rectangular 2 × 35 μm
2
spin-valve
elements were defined by direct-write laser lithography (DWL2.0 Heidelberg, 405 nm
wavelength diode laser) followed by ion milling (Nordiko 3600 tool, using a 0.16
A/cm
2
Ar+ beam). The metallic leads were patterned to connect 6 elements in series and
therefore defining the sensor array. The metal contacts consist of 300 nm thick Al
98.6
Si
1.0
Cu
0.4
film deposited by sputtering in a Nordiko 7000 tool (2 kW, 50 sccm Argon and 3.0
mTorr), capped by 20 nm Ru film in Nordiko 3600 to improve electrical contact, patterned
by laser lithography and defined by lift-off. Finally, the sensor chip surface was passivated
Figure 2.
The robot, the finger, the sensor, the sensing elements, and their integration. (
a
) Vizzy; (
b
) a finger from Vizzy
with three phalanges, each with a tactile sensor; (
c
) redesigned middle phalange part to integrate (
d
) the electronic interface
and (e) flexible hybrid device with the flexible sensing matrix and the (f) Si chips with the spin-valve sensors.
3.2. Rigid Chips with the Magnetoresistive Sensors
The magnetic sensor elements are fabricated in large wafers using industrial pro-
cessing tools at INESC-MN, individualized using a DAD 321 dicing system to their final
dimensions of 0.8
×
1.5 mm
2
. After dicing, the sensors are characterized individually and
selected, as a quality control measure, to secure the device performance across the eight
chips in the sensing matrix. This process versatility is not available in printing or many
other flexible technologies, relying on all the sensors to be viable in a matrix. The geometry
for the sensors produced is shown in Figure 3.
Sensors 2021,21, 5098 5 of 23
Sensors 2021, 21, x FOR PEER REVIEW 5 of 25
with a 100 nm Al
2
O
3
layer deposited by magnetron sputtering, except over the contact
pads, for further protection of the sensing elements of encapsulation.
Figure 3. (a) Si chips facing down and connected to the FPC. (b) Top view of the microfabricated 6 spin valve sensor in
series, where each is 2 × 35 μm
2
. The arrow to the left of the sensor series side indicates the sensitive direction of these. (c)
Cross-section schematic of the rigid Si chip.
3.3. Flexible Printed Circuit Board
A flexible printed circuit cable (FPC) was fabricated using a laminated foil with 25
μm thick polyimide and 9 μm of copper, patterned by laser lithography and wet etch.
The FPC was designed to fully cover the finger surface, the most straightforward way
to achieve this is to connect the left and the right side of the finger part with a “strip”. The
result is an FPC that distributes 8 sensors in a 4 × 2 matrix with 2 mm in between as pre-
sented in Figure 4. The distance is much lower than previously reported designs because
we could fabricate them without packaging. Moreover, the mechanical flexibility allows
us to conform the FPC to the finger surface shape resulting in mounting architecture
shown in Figure 5c,d.
Figure 3.
(
a
) Si chips facing down and connected to the FPC. (
b
) Top view of the microfabricated 6 spin valve sensor in
series, where each is 2
×
35
µ
m
2
. The arrow to the left of the sensor series side indicates the sensitive direction of these.
(c) Cross-section schematic of the rigid Si chip.
The sensing element consists of a top-pinned spin-valve sensor microfabricated on
top of a Si wafer, with the following stack (thickness in nm): Si/SiO
2
100/Ta 1/NiFe
2.8/CoFe 2.5/Cu 2.6/CoFe 2.3/MnIr 18/Ta 3 deposited by ion beam sputtering in a
Nordiko 3000 tool [
29
]. Notice that both sides of the wafer are coated with the same SiO
2
thermal oxide to minimize leakage currents through the final device’s substrate.
To obtain a linear response from the sensor, the rectangular 2
×
35
µ
m
2
spin-valve
elements were defined by direct-write laser lithography (DWL2.0 Heidelberg, 405 nm
wavelength diode laser) followed by ion milling (Nordiko 3600 tool, using a 0.16 A/cm
2
Ar+
beam). The metallic leads were patterned to connect 6 elements in series and therefore
defining the sensor array. The metal contacts consist of 300 nm thick Al
98.6
Si
1.0
Cu
0.4
film deposited by sputtering in a Nordiko 7000 tool (2 kW, 50 sccm Argon and 3.0 mTorr),
capped by 20 nm Ru film in Nordiko 3600 to improve electrical contact, patterned by laser
lithography and defined by lift-off. Finally, the sensor chip surface was passivated with a
100 nm Al
2
O
3
layer deposited by magnetron sputtering, except over the contact pads, for
further protection of the sensing elements of encapsulation.
3.3. Flexible Printed Circuit Board
A flexible printed circuit cable (FPC) was fabricated using a laminated foil with 25
µ
m
thick polyimide and 9 µm of copper, patterned by laser lithography and wet etch.
The FPC was designed to fully cover the finger surface, the most straightforward way
to achieve this is to connect the left and the right side of the finger part with a “strip”.
The result is an FPC that distributes 8 sensors in a 4
×
2 matrix with 2 mm in between
as presented in Figure 4. The distance is much lower than previously reported designs
because we could fabricate them without packaging. Moreover, the mechanical flexibility
allows us to conform the FPC to the finger surface shape resulting in mounting architecture
shown in Figure 5c,d.
Sensors 2021,21, 5098 6 of 23
Sensors 2021, 21, x FOR PEER REVIEW 5 of 25
with a 100 nm Al
2
O
3
layer deposited by magnetron sputtering, except over the contact
pads, for further protection of the sensing elements of encapsulation.
Figure 3. (a) Si chips facing down and connected to the FPC. (b) Top view of the microfabricated 6 spin valve sensor in
series, where each is 2 × 35 μm
2
. The arrow to the left of the sensor series side indicates the sensitive direction of these. (c)
Cross-section schematic of the rigid Si chip.
3.3. Flexible Printed Circuit Board
A flexible printed circuit cable (FPC) was fabricated using a laminated foil with 25
μm thick polyimide and 9 μm of copper, patterned by laser lithography and wet etch.
The FPC was designed to fully cover the finger surface, the most straightforward way
to achieve this is to connect the left and the right side of the finger part with a “strip”. The
result is an FPC that distributes 8 sensors in a 4 × 2 matrix with 2 mm in between as pre-
sented in Figure 4. The distance is much lower than previously reported designs because
we could fabricate them without packaging. Moreover, the mechanical flexibility allows
us to conform the FPC to the finger surface shape resulting in mounting architecture
shown in Figure 5c,d.
Figure 4.
(
a
) Top view of the FPC, after defining the copper layer; (
b
) microfabrication steps: 1. Bare copper/polyimide film
cleaning, 2. Coating and definition of photoresist using lithography system, 3. Wet etch and resist stripping.
Sensors 2021, 21, x FOR PEER REVIEW 6 of 25
Figure 4. (a) Top view of the FPC, after defining the copper layer; (b) microfabrication steps: 1. Bare copper/polyimide
film cleaning, 2. Coating and definition of photoresist using lithography system, 3. Wet etch and resist stripping.
The bonding of the Si chip to the FPC is an important development step for this fab-
rication process. The bonding must guarantee a good electrical contact and strong me-
chanical adhesion. For this design geometry to have a minimal footprint the Si chip must
be flipped to contact the FPC (Figure 5b). Soldering Si chips facing down was not an op-
tion, and any other manual approach would have a higher footprint making it impossible
to reach such high densities. Moreover, the sensor’s exposure to high temperatures (over
200 °C) will lead to signal loss from exchange bias weakening and inter-layer diffusion.
The first can be reversed by cooling the sensors in a 1 T magnetic field, while the second
is irreversible. Ensuring all the sensors are exposed to the same thermal cycle helps us
guarantee that the signal has less influence from thermal issues. The sensor chips were
flipped and glued to the FPC using a silver conductive epoxy adhesive (MG Chemicals
8331). The main challenges of this step are the manual manipulation of the relatively small
Si chips and the lack of an accurate epoxy volume control. The manual manipulation can
lead to two main assembly errors: the distance between the sensor to be smaller or larger
than the 2 mm they were dimensioned to be; and a relative angle between them. The tested
devices were assembled with a precision of ~200 μm, and angle errors under 10°. A better
epoxy volume could allow us to better understand the influence of thickness and mechan-
ical and electrical quality of the contact.
Figure 5. (a) Top view of the FPC, showing the pads to connect the Si chips; (b) Si chip in the FPC using the epoxy glue;
(c) 3D view of the FPC with the Si chips embedded in the PDMS part. (d) Cross section of the device showing the curvature
for the flexible sensing matrix. (e) A zoom in on a Si chip glued to the FPC.
3.4. Polymeric Finger Part
Finally, the FPC and the sensors were embedded in an elastomer cap, shaped with
similar curvature as the FPC, to protect it from the environment and provide the robot
Figure 5.
(
a
) Top view of the FPC, showing the pads to connect the Si chips; (
b
) Si chip in the FPC using the epoxy glue;
(
c
) 3D view of the FPC with the Si chips embedded in the PDMS part. (
d
) Cross section of the device showing the curvature
for the flexible sensing matrix. (e) A zoom in on a Si chip glued to the FPC.
The bonding of the Si chip to the FPC is an important development step for this
fabrication process. The bonding must guarantee a good electrical contact and strong
mechanical adhesion. For this design geometry to have a minimal footprint the Si chip
must be flipped to contact the FPC (Figure 5b). Soldering Si chips facing down was not an
option, and any other manual approach would have a higher footprint making it impossible
to reach such high densities. Moreover, the sensor’s exposure to high temperatures (over
200
◦
C) will lead to signal loss from exchange bias weakening and inter-layer diffusion.
The first can be reversed by cooling the sensors in a 1 T magnetic field, while the second
is irreversible. Ensuring all the sensors are exposed to the same thermal cycle helps us
guarantee that the signal has less influence from thermal issues. The sensor chips were
flipped and glued to the FPC using a silver conductive epoxy adhesive (MG Chemicals
8331). The main challenges of this step are the manual manipulation of the relatively small
Sensors 2021,21, 5098 7 of 23
Si chips and the lack of an accurate epoxy volume control. The manual manipulation can
lead to two main assembly errors: the distance between the sensor to be smaller or larger
than the 2 mm they were dimensioned to be; and a relative angle between them. The
tested devices were assembled with a precision of ~200
µ
m, and angle errors under 10
◦
. A
better epoxy volume could allow us to better understand the influence of thickness and
mechanical and electrical quality of the contact.
3.4. Polymeric Finger Part
Finally, the FPC and the sensors were embedded in an elastomer cap, shaped with similar
curvature as the FPC, to protect it from the environment and provide the robot with better
grasping. A Witbox 3D printer fabricated the mold using a PLA filament and a layer height of
0.2 mm. The FSM was attached to the molds, holding it in place while also serving as a casting
mold for shaping the silicone cap (Figure 6). The curing of the polymer coincided with the
FPC and polymer bonding. The elastomer used was PDMS (polydimethylsiloxane) in a 1:15
proportion and cured at 70
◦
C for 1 h. This temperature does not affect the magnetoresistive
sensor, demonstrating thermal stability up to 120 ◦C [31].
Sensors 2021, 21, x FOR PEER REVIEW 7 of 25
with better grasping. A Witbox 3D printer fabricated the mold using a PLA filament and
a layer height of 0.2 mm. The FSM was attached to the molds, holding it in place while
also serving as a casting mold for shaping the silicone cap (Figure 6). The curing of the
polymer coincided with the FPC and polymer bonding. The elastomer used was PDMS
(polydimethylsiloxane) in a 1:15 proportion and cured at 70 °C for 1 h. This temperature
does not affect the magnetoresistive sensor, demonstrating thermal stability up to 120 °C
[31].
Figure 6. Two-part 3D printed mold used to shape the elastomeric part and conform the FPC with
the Si chips.
3.5. Electronic Interface
The finger part chosen to test this approach was the middle phalange of Vizzy’s fin-
ger (see Figure 7a). This phalange has the lowest active area, among all phalanges, result-
ing in the lowest number of sensors to cover the surface and thus simpler electronics. The
design and development of a custom-made solution benefits from the flexibility in design
but raises some challenges regarding integration, specifically the electronic interface.
First, we had to make sure the electronics could fit the finger part without restraining
any finger, hand, or arm movements. We redesigned the aluminum part at a 1:1 scale
using a PLA filament and a Witbox 3D printer (Figure 7b) to fit two extra FPCs one for
each side. The left FPC (L-00) is shown in Figure 7(c.1) and the right FPC (R-11) is shown
in Figure 7(c.3). These two extra FPCs are used to convert the analog signals from the
flexible sensor matrix with the 8 Si chips (described in the previous section) and output
into the standard I2C communication interface (GND, VCC, SDA, and SCL).
To connect the three boards to each other, we used vertical flex connectors, one for
each board on the side of the finger. In order to capture the data from the sensor on each
lateral FPC the main component of interest is the ADS122C04 which has a 24-bit ADC, a
current source and a multiplexer. The ADS122C04 requires two 1 kΩ 0402 resistors and
two 0402 100 nF capacitors, to enable the readout of 4 individual sensors with a noise
peak-to-peak (Noise
P-P
) level of 25 μV, at 10 samples per second per sensor with 1 mA
current.
The digital signal output is interpreted by an Arduino MRK1000, which is responsi-
ble for communicating with the device and connecting to a computer via USB, where the
data is analyzed.
Figure 6.
Two-part 3D printed mold used to shape the elastomeric part and conform the FPC with
the Si chips.
3.5. Electronic Interface
The finger part chosen to test this approach was the middle phalange of Vizzy’s finger
(see Figure 7a). This phalange has the lowest active area, among all phalanges, resulting in
the lowest number of sensors to cover the surface and thus simpler electronics. The design
and development of a custom-made solution benefits from the flexibility in design but
raises some challenges regarding integration, specifically the electronic interface.
First, we had to make sure the electronics could fit the finger part without restraining
any finger, hand, or arm movements. We redesigned the aluminum part at a 1:1 scale using
a PLA filament and a Witbox 3D printer (Figure 7b) to fit two extra FPCs one for each
side. The left FPC (L-00) is shown in Figure 7(c.1) and the right FPC (R-11) is shown in
Figure 7(c.3)
. These two extra FPCs are used to convert the analog signals from the flexible
sensor matrix with the 8 Si chips (described in the previous section) and output into the
standard I2C communication interface (GND, VCC, SDA, and SCL).
To connect the three boards to each other, we used vertical flex connectors, one for each
board on the side of the finger. In order to capture the data from the sensor on each lateral
FPC the main component of interest is the ADS122C04 which has a 24-bit ADC, a current
source and a multiplexer. The ADS122C04 requires two 1 k
Ω
0402 resistors and two 0402
100 nF capacitors, to enable the readout of 4 individual sensors with a noise peak-to-peak
(NoiseP-P) level of 25 µV, at 10 samples per second per sensor with 1 mA current.
The digital signal output is interpreted by an Arduino MRK1000, which is responsible
for communicating with the device and connecting to a computer via USB, where the data
is analyzed.
Sensors 2021,21, 5098 8 of 23
Sensors 2021, 21, x FOR PEER REVIEW 8 of 25
Figure 7. (a) Vizzy’s finger is made of aluminum and is compatible with Figure 1a and is described in detail in a previous
work [6]. (b) Redesigned 3D printed prototype for the middle phalange, the electronic interface, and the tactile sensor. (c)
The prototyped finger part without the tactile sensor, detailing the flex connectors (c.2); the FPC (L-00) on the left side (c.1)
and the FPC (R-11) on the right side (c.3); (d) detail of the L-00 FPC, identifying the flex connector and the analog inputs,
the ADS122c04, resistors, capacitor, and the I2C output
.
4. Sensor Characterization
4.1. Si Chip
Figure 7.
(
a
) Vizzy’s finger is made of aluminum and is compatible with Figure 1a and is described in detail in a previous
work [
6
]. (
b
) Redesigned 3D printed prototype for the middle phalange, the electronic interface, and the tactile sensor.
(
c
) The prototyped finger part without the tactile sensor, detailing the flex connectors (
c.2
); the FPC (L-00) on the left side
(
c.1
) and the FPC (R-11) on the right side (
c.3
); (
d
) detail of the L-00 FPC, identifying the flex connector and the analog
inputs, the ADS122c04, resistors, capacitor, and the I2C output.
Sensors 2021,21, 5098 9 of 23
4. Sensor Characterization
4.1. Si Chip
The geometrical constraints to fit the electronics in the finger limits the size of the
electronic interface significantly. The ADS122c04 size (4.5
×
5 mm) occupies most of the
available space itself (Figure 7), so including an amplifier was not considered possible.
Without an amplifier the only choice left is to use the current to amplify the signal. The
ADC has a saturation voltage of 2.048 V and can provide a maximum of 1.5 mA as current
bias. Using a 1 mA current and 2 k
Ω
, sensor can maximize the sensor output without
saturating the ADC. Therefore, we have the six spin-valve series that are tailored to have
a total resistance of 2 k
Ω
, and a linear range of at least
−
1 to 1 mT. The series of six
2×35 µm2
fabricated sensors have a sensitivity of 0.72%/mT and a resistance of 2 k
Ω
. So,
the electrical range of operation for the sensor in this application will be from 1.985 V for
−1 mT to 2.015 V for 1 mT with a 1 mA applied current.
The transfer curve of MR(H) of all eight sensors, L1–R4, displays a clear excellent
uniformity, with an average sensitivity of 0.72%/mT (14.1 mV/mT) and the moderate
magnetoresistance values MR ~5% consistent with excessive contact resistance in these
series connection architectures (Figure 8). The spin-valve sensors were characterized at
wafer level. The sensor bias current (1 mA) was supplied by a Keithley 220 programmable
current source and the voltage measured by a Keithley 182 sensitive digital voltmeter,
while a KEPCO bipolar power supply was used to set the current to the Helmholtz coils
during the transfer curve MR(H) characterization. The chips used in this work show
resistance in the saturation state Rmin ~2 k
Ω
, coercivity Hc <0.1 mT, and the transfer
curves are centered around H = 0 (small shift <0.4 mT, caused by the Neel coupling and
demagnetizing fields [32]) and a linear range of ±2.5 mT.
Sensors 2021, 21, x FOR PEER REVIEW 9 of 25
The geometrical constraints to fit the electronics in the finger limits the size of the
electronic interface significantly. The ADS122c04 size (4.5 × 5 mm) occupies most of the
available space itself (Figure 7), so including an amplifier was not considered possible.
Without an amplifier the only choice left is to use the current to amplify the signal. The
ADC has a saturation voltage of 2.048 V and can provide a maximum of 1.5 mA as current
bias. Using a 1 mA current and 2 kΩ, sensor can maximize the sensor output without
saturating the ADC. Therefore, we have the six spin-valve series that are tailored to have
a total resistance of 2 kΩ, and a linear range of at least −1 to 1 mT. The series of six 2 × 35
μm
2
fabricated sensors have a sensitivity of 0.72%/mT and a resistance of 2 kΩ. So, the
electrical range of operation for the sensor in this application will be from 1.985 V for −1
mT to 2.015 V for 1 mT with a 1 mA applied current.
The transfer curve of MR(H) of all eight sensors, L1–R4, displays a clear excellent
uniformity, with an average sensitivity of 0.72%/mT (14.1 mV/mT) and the moderate mag-
netoresistance values MR ~ 5% consistent with excessive contact resistance in these series
connection architectures (Figure 8). The spin-valve sensors were characterized at wafer
level. The sensor bias current (1 mA) was supplied by a Keithley 220 programmable cur-
rent source and the voltage measured by a Keithley 182 sensitive digital voltmeter, while
a KEPCO bipolar power supply was used to set the current to the Helmholtz coils during
the transfer curve MR(H) characterization. The chips used in this work show resistance in
the saturation state Rmin ~ 2 kΩ, coercivity Hc < 0.1 mT, and the transfer curves are cen-
tered around H = 0 (small shift < 0.4 mT, caused by the Neel coupling and demagnetizing
fields [32]) and a linear range of ±2.5 mT.
Figure 8. Transfer curve MR(H) curve of the used Si chips within a field range of ±10 mT with a 1 mA current. The current
to the sensor was supplied by a Keithley 220 programmable current source and the voltage was measured with a Keithley
182 sensitive digital voltmeter, while the current provided to the Helmholtz coils responsible for controlling the applied
field (μ
0
.H) is a KEPCO bipolar operational power supply.
4.2. Si Chip Bonding to the FPC
The use of conductive epoxy to bond the Si chips to the FPC described in Section 3.3
brought challenges such as controlling the volume of glue used per pad, the chip align-
ment, and the quality of the electrical contact. The quality of electrical contact can limit
the detectivity. The ADC manufacturer reports a 20 μV noise peak to peak, which for this
design, we could expect a 1.4 μT minimum detectable field.
Figure 8.
Transfer curve MR(H) curve of the used Si chips within a field range of
±
10 mT with a 1 mA current. The current
to the sensor was supplied by a Keithley 220 programmable current source and the voltage was measured with a Keithley
182 sensitive digital voltmeter, while the current provided to the Helmholtz coils responsible for controlling the applied
field (µ0.H) is a KEPCO bipolar operational power supply.
4.2. Si Chip Bonding to the FPC
The use of conductive epoxy to bond the Si chips to the FPC described in
Section 3.3
brought challenges such as controlling the volume of glue used per pad, the chip alignment,
and the quality of the electrical contact. The quality of electrical contact can limit the
Sensors 2021,21, 5098 10 of 23
detectivity. The ADC manufacturer reports a 20
µ
V noise peak to peak, which for this
design, we could expect a 1.4 µT minimum detectable field.
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65
◦
C, or 7 min at 125
◦
C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower temperature,
the curing was performed up to 60 min without significant improvement of the quality
of the electric contact. However, only 10 min at 150
◦
C (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
) provided an electrical contact
quality as good as when measuring directly on the contact pads of the chip. Lower Noise
P2P
was achieved by increasing the temperature to 250
◦
C and time to 30 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
), resulting
in a better contact quality than provided by the probes placed on the contact pads of the
Si chip. Higher temperatures and longer time make the magnetoresistive sensor prone to
inter-layer diffusion and consequent loss of signal, so these are suitable process parameters
for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
Figure 9.
Comparison of different curing procedures. We show the voltage variation (
µ
V) of the
sensor used in L1 during a 300 s measurement at H = 0 T using a Keithley 220 as a current source,
Keithley 182 voltmeter, and micrometric probes to make contact with: the contact pads of the Si chip
(
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
), the FPC with the Si chip bonded with the epoxy cured at 70
◦
C for 20 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
), 40 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
) and
60 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
), cured at 150 ◦C for 10 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
) and 250 ◦C for 30 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
).
When comparing the Noise
P2P
before the bonding process (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
) and the final device (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
),
an average increase of 25% is observed across all sensors (Figure 10), which considering
the benefits of integration, is considered an acceptable trade-off.
Sensors 2021,21, 5098 11 of 23
Sensors 2021, 21, x FOR PEER REVIEW 11 of 25
Figure 10. Comparison between the noise
P2P
(μV), for all eight sensors (L1–R4), at H = 0 T using a Keithley 220 as a current
source, Keithley 182 voltmeter, and micrometric probes directly on to the contact pads of the Si chip ( ), the FPC with the
Si chip bonded with the epoxy cured at 250 °C for 30 min ( ) and the same but using the electronic interface described
in Section 3.5 ( ).
The data rate (samples per second) at which one can retrieve the data from the sensor
is also an important parameter. The reaction time is defined by the sum of the acquisition
time, the processing time, and actuation time. Reducing the acquisition time requires
higher samples per second (SPS). An increase in the noise
P2P
is expected due to the sigma-
delta ADC working principle and is characterized in Figure 11. To measure at 2000 sam-
ples per second, the detectivity changes by a factor of 8, from 5 μT to 38 μT (Figure 11). A
±25 μT reference point (dashed line) and the scale for converting to an equivalent magnetic
field (mT) was added. The earth’s magnetic field can vary from ±30 to 50 μT and would
still be detectable for a 2000 SPS rate. However, it limits the device’s ability to detect lower
sensor displacements, and consequently, the minimum detectable force of the device.
Figure 10.
Comparison between the noise
P2P
(
µ
V), for all eight sensors (L1–R4), at H = 0 T using a
Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes directly on to the
contact pads of the Si chip (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
), the FPC with the Si chip bonded with the epoxy cured at 250
◦
C for
30 min (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
) and the same but using the electronic interface described in Section 3.5 (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
).
The data rate (samples per second) at which one can retrieve the data from the sensor
is also an important parameter. The reaction time is defined by the sum of the acquisition
time, the processing time, and actuation time. Reducing the acquisition time requires higher
samples per second (SPS). An increase in the noise
P2P
is expected due to the sigma-delta
ADC working principle and is characterized in Figure 11. To measure at 2000 samples per
second, the detectivity changes by a factor of 8, from 5
µ
T to 38
µ
T (Figure 11). A
±
25
µ
T
reference point (dashed line) and the scale for converting to an equivalent magnetic field
(mT) was added. The earth’s magnetic field can vary from
±
30 to 50
µ
T and would still be
detectable for a 2000 SPS rate. However, it limits the device’s ability to detect lower sensor
displacements, and consequently, the minimum detectable force of the device.
Sensors 2021, 21, x FOR PEER REVIEW 12 of 25
Figure 11. Noise
P2P
(μV) for sensor L1 at H = 0 T in the final device ( ) for different data rates.
-500
-400
-300
-200
-100
0
100
200
300
400
500
NoiseP2P
170.04 µV
NoiseP2P
535.73 µV
NoiseP2P
473.41 µV
NoiseP2P
444.88 µV
NoiseP2P
72.74 µV
NoiseP2P
90.56 µV
NoiseP2P
145.52 µV
ΔV (μV)
1200 SPS
40 SPS 90 SPS 180 SPS 360 SPS 660 SPS 2000 SPS
(1)
Earth Magnetic Field
Time (60 s measurement)
25 μT
(1)
Equivalent Field (mT)
-0.035
-0.030
-0.025
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
Figure 11. NoiseP2P (µV) for sensor L1 at H = 0 T in the final device (
Sensors 2021, 21, x FOR PEER REVIEW 10 of 25
The pot life of this epoxy is about 10 min, which added an extra layer of difficulty
during the Si chips’ manual placement on the FPC.
The silver conductive epoxy adhesive recommended curing instructions are: 24-h at
room temperature, 15 min at 65 °C, or 7 min at 125 °C. To maximize the throughput, the
24 h curing at room temperature procedure was not considered. For the lower tempera-
ture, the curing was performed up to 60 min without significant improvement of the qual-
ity of the electric contact. However, only 10 min at 150 °C ( ) provided an electrical con-
tact quality as good as when measuring directly on the contact pads of the chip. Lower
Noise
P2P
was achieved by increasing the temperature to 250 °C and time to 30 min ( ),
resulting in a better contact quality than provided by the probes placed on the contact
pads of the Si chip. Higher temperatures and longer time make the magnetoresistive sen-
sor prone to inter-layer diffusion and consequent loss of signal, so these are suitable pro-
cess parameters for bonding.
4.3. Electronic Interface
The Si chips bonded to the FPC characterized in Figure 9, were then embedded in the
PDMS as described in 3.4 and connected to the electronic interface detailed in 3.5.
When comparing the Noise
P2P
before the bonding process ( ) and the final device (
), an average increase of 25% is observed across all sensors (Figure 10), which consider-
ing the benefits of integration, is considered an acceptable trade-off.
Figure 9. Comparison of different curing procedures. We show the voltage variation (μV) of the sensor used in L1 during
a 300 s measurement at H = 0 T using a Keithley 220 as a current source, Keithley 182 voltmeter, and micrometric probes
to make contact with: the contact pads of the Si chip ( ), the FPC with the Si chip bonded with the epoxy cured at 70 °C
for 20 min ( ), 40 min ( ) and 60 min ( ), cured at 150 °C for 10 min ( ) and 250 °C for 30 min ( ).
0150
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
100
200
300
400
500
600
700
800
900
1000
NoiseP2P
651.51 μV
NoiseP2P
28.66 μV
NoiseP2P
50.05 μV
NoiseP2P
44.55 µV
NoiseP2P
651.51 μVNoiseP2P
619.95 μV
ΔV (μV)
0150
B5
B4
B3
B2
0 150
60min@70deg
A
Time (s)
20min@70deg 40min@70deg 10min@150deg 30min@250deg
0150
0150
B1
0150
) for different data rates.
Sensors 2021,21, 5098 12 of 23
4.4. Experimental Setup
The transport curve and electrical characterization are essential for fabrication and
manufacturing control but do not provide clear insights into the sensor operational lim-
itations. Fully integrating the sensor on the robot hand or just the finger is a complex
and time-consuming task, so it becomes valuable to test the tactile sensors as close to the
application conditions as possible to an actual situation. Therefore, a setup that could
apply and measure forces precisely is vital for further optimizing and developing of the
tactile sensor. We assembled a setup consisting of the magnetic tactile sensor, a three-axis
cartesian motorized stage (Thorlabs DDS220), and a 6-degree force sensor (ATI nano 17).
The tactile sensor is fixed on the stage using a 3D printed part (Figure 12(a.1,a.2)).
Sensors 2021, 21, x FOR PEER REVIEW 13 of 25
4.4. Experimental Setup
The transport curve and electrical characterization are essential for fabrication and
manufacturing control but do not provide clear insights into the sensor operational limi-
tations. Fully integrating the sensor on the robot hand or just the finger is a complex and
time-consuming task, so it becomes valuable to test the tactile sensors as close to the ap-
plication conditions as possible to an actual situation. Therefore, a setup that could apply
and measure forces precisely is vital for further optimizing and developing of the tactile
sensor. We assembled a setup consisting of the magnetic tactile sensor, a three-axis carte-
sian motorized stage (Thorlabs DDS220), and a 6-degree force sensor (ATI nano 17). The
tactile sensor is fixed on the stage using a 3D printed part (Figure 12(a.1,a.2)).
Figure 12. (a) The aluminum rod in a cantilever configuration, the ATI nano 17, the magnetic tactile
sensor, and the stage; (a.1) shows a 3D printed part (left) to fix the tactile sensor (right) to the stage;
(a.2) shows how the 3D printed part fits with the tactile sensor. (b) Force equilibrium and working
principles of the setup, where the identifies data from the ATI nano 17
(
,
,
,
,
and
)
, the data from both L-00 and R-11 lateral FPCS of the eight sensors (L1–R4) and the data
from the PC to the three servo motors Thorlabs stage controlling the displacement.
The ATI nano 17 is a multi-axis force and torque sensor system that is able to measure
both forces (, , and ) and torques (, ,and ). In this setup we attached one to
an aluminum cantilever fixed to the table. A computer communicates with the stage,
providing instructions to align and press the sensors against each other while reading
their output. This setup configuration allows an evaluation of the sensor performance un-
der normal and shear forces. For this case, the tactile sensor curved surface top-center
point was aligned with the ATI nano sensor, and the stage presses the sensors against each
other.
The sensors are aligned visually in such a way that the tactile sensor touches the ATI
nano center. The alignment results from actuating the stage and visually checking
whether they are centered in X and Y directions and touching in the Z direction. After
alignment, the stage moves vertically in steps of 0.05 mm up to 1 mm, pressing the sensors
against each other. The recorded values from the ATI nano and tactile sensor for each step
are plotted in Figure 13a,b, respectively. The load phase corresponding to pressing the
tactile and force sensors against each other is followed by an unload phase, where the
opposite occurs.
The stage position changes every 30 s providing the time frame to record the sensor’s
output. The values presented in Figure 13(d.1–d.4) show the average value for each posi-
tion. The ATI nano output already considers a calibration from the manufacturer to pro-
vide the force and torque output in Figure 13a. The tactile sensor output comprises indi-
vidual voltage measurements of the eight Si chips. We use the percentage of change in
resistance, ΔResistance (%), to quantify the applied magnetic field change (equivalent
field change (μT)).
Figure 12.
(
a
) The aluminum rod in a cantilever configuration, the ATI nano 17, the magnetic tactile
sensor, and the stage; (
a.1
) shows a 3D printed part (left) to fix the tactile sensor (right) to the stage;
(
a.2
) shows how the 3D printed part fits with the tactile sensor. (
b
) Force equilibrium and working
principles of the setup, where the
Sensors 2021, 21, x FOR PEER REVIEW 13 of 25
4.4. Experimental Setup
The transport curve and electrical characterization are essential for fabrication and
manufacturing control but do not provide clear insights into the sensor operational limi-
tations. Fully integrating the sensor on the robot hand or just the finger is a complex and
time-consuming task, so it becomes valuable to test the tactile sensors as close to the ap-
plication conditions as possible to an actual situation. Therefore, a setup that could apply
and measure forces precisely is vital for further optimizing and developing of the tactile
sensor. We assembled a setup consisting of the magnetic tactile sensor, a three-axis carte-
sian motorized stage (Thorlabs DDS220), and a 6-degree force sensor (ATI nano 17). The
tactile sensor is fixed on the stage using a 3D printed part (Figure 12(a.1,a.2)).
Figure 12. (a) The aluminum rod in a cantilever configuration, the ATI nano 17, the magnetic tactile
sensor, and the stage; (a.1) shows a 3D printed part (left) to fix the tactile sensor (right) to the stage;
(a.2) shows how the 3D printed part fits with the tactile sensor. (b) Force equilibrium and working
principles of the setup, where the identifies data from the ATI nano 17
(
,
,
,
,
and
)
, the data from both L-00 and R-11 lateral FPCS of the eight sensors (L1–R4) and the data
from the PC to the three servo motors Thorlabs stage controlling the displacement.
The ATI nano 17 is a multi-axis force and torque sensor system that is able to measure
both forces (, , and ) and torques (, ,and ). In this setup we attached one to
an aluminum cantilever fixed to the table. A computer communicates with the stage,
providing instructions to align and press the sensors against each other while reading
their output. This setup configuration allows an evaluation of the sensor performance un-
der normal and shear forces. For this case, the tactile sensor curved surface top-center
point was aligned with the ATI nano sensor, and the stage presses the sensors against each
other.
The sensors are aligned visually in such a way that the tactile sensor touches the ATI
nano center. The alignment results from actuating the stage and visually checking
whether they are centered in X and Y directions and touching in the Z direction. After
alignment, the stage moves vertically in steps of 0.05 mm up to 1 mm, pressing the sensors
against each other. The recorded values from the ATI nano and tactile sensor for each step
are plotted in Figure 13a,b, respectively. The load phase corresponding to pressing the
tactile and force sensors against each other is followed by an unload phase, where the
opposite occurs.
The stage position changes every 30 s providing the time frame to record the sensor’s
output. The values presented in Figure 13(d.1–d.4) show the average value for each posi-
tion. The ATI nano output already considers a calibration from the manufacturer to pro-
vide the force and torque output in Figure 13a. The tactile sensor output comprises indi-
vidual voltage measurements of the eight Si chips. We use the percentage of change in
resistance, ΔResistance (%), to quantify the applied magnetic field change (equivalent
field change (μT)).
identifies data from the ATI nano 17 (
Fx
,
Fy
,
Fz
,
Tx
,
Ty
and
Tz
),
Sensors 2021, 21, x FOR PEER REVIEW 13 of 25
4.4. Experimental Setup
The transport curve and electrical characterization are essential for fabrication and
manufacturing control but do not provide clear insights into the sensor operational limi-
tations. Fully integrating the sensor on the robot hand or just the finger is a complex and
time-consuming task, so it becomes valuable to test the tactile sensors as close to the ap-
plication conditions as possible to an actual situation. Therefore, a setup that could apply
and measure forces precisely is vital for further optimizing and developing of the tactile
sensor. We assembled a setup consisting of the magnetic tactile sensor, a three-axis carte-
sian motorized stage (Thorlabs DDS220), and a 6-degree force sensor (ATI nano 17). The
tactile sensor is fixed on the stage using a 3D printed part (Figure 12(a.1,a.2)).
Figure 12. (a) The aluminum rod in a cantilever configuration, the ATI nano 17, the magnetic tactile
sensor, and the stage; (a.1) shows a 3D printed part (left) to fix the tactile sensor (right) to the stage;
(a.2) shows how the 3D printed part fits with the tactile sensor. (b) Force equilibrium and working
principles of the setup, where the identifies data from the ATI nano 17
(
,
,
,
,
and
)
, the data from both L-00 and R-11 lateral FPCS of the eight sensors (L1–R4) and the data
from the PC to the three servo motors Thorlabs stage controlling the displacement.
The ATI nano 17 is a multi-axis force and torque sensor system that is able to measure
both forces (, , and ) and torques (, ,and ). In this setup we attached one to
an aluminum cantilever fixed to the table. A computer communicates with the stage,
providing instructions to align and press the sensors against each other while reading
their output. This setup configuration allows an evaluation of the sensor performance un-
der normal and shear forces. For this case, the tactile sensor curved surface top-center
point was aligned with the ATI nano sensor, and the stage presses the sensors against each
other.
The sensors are aligned visually in such a way that the tactile sensor touches the ATI
nano center. The alignment results from actuating the stage and visually checking
whether they are centered in X and Y directions and touching in the Z direction. After
alignment, the stage moves vertically in steps of 0.05 mm up to 1 mm, pressing the sensors
against each other. The recorded values from the ATI nano and tactile sensor for each step
are plotted in Figure 13a,b, respectively. The load phase corresponding to pressing the
tactile and force sensors against each other is followed by an unload phase, where the
opposite occurs.
The stage position changes every 30 s providing the time frame to record the sensor’s
output. The values presented in Figure 13(d.1–d.4) show the average value for each posi-
tion. The ATI nano output already considers a calibration from the manufacturer to pro-
vide the force and torque output in Figure 13a. The tactile sensor output comprises indi-
vidual voltage measurements of the eight Si chips. We use the percentage of change in
resistance, ΔResistance (%), to quantify the applied magnetic field change (equivalent
field change (μT)).
the data from both L-00 and R-11 lateral FPCS of the eight sensors (L1–R4) and
Sensors 2021, 21, x FOR PEER REVIEW 13 of 25
4.4. Experimental Setup
The transport curve and electrical characterization are essential for fabrication and
manufacturing control but do not provide clear insights into the sensor operational limi-
tations. Fully integrating the sensor on the robot hand or just the finger is a complex and
time-consuming task, so it becomes valuable to test the tactile sensors as close to the ap-
plication conditions as possible to an actual situation. Therefore, a setup that could apply
and measure forces precisely is vital for further optimizing and developing of the tactile
sensor. We assembled a setup consisting of the magnetic tactile sensor, a three-axis carte-
sian motorized stage (Thorlabs DDS220), and a 6-degree force sensor (ATI nano 17). The
tactile sensor is fixed on the stage using a 3D printed part (Figure 12(a.1,a.2)).
Figure 12. (a) The aluminum rod in a cantilever configuration, the ATI nano 17, the magnetic tactile
sensor, and the stage; (a.1) shows a 3D printed part (left) to fix the tactile sensor (right) to the stage;
(a.2) shows how the 3D printed part fits with the tactile sensor. (b) Force equilibrium and working
principles of the setup, where the identifies data from the ATI nano 17
(
,
,
,
,
and
)
, the data from both L-00 and R-11 lateral FPCS of the eight sensors (L1–R4) and the data
from the PC to the three servo motors Thorlabs stage controlling the displacement.
The ATI nano 17 is a multi-axis force and torque sensor system that is able to measure
both forces (, , and ) and torques (, ,and ). In this setup we attached one to
an aluminum cantilever fixed to the table. A computer communicates with the stage,
providing instructions to align and press the sensors against each other while reading
their output. This setup configuration allows an evaluation of the sensor performance un-
der normal and shear forces. For this case, the tactile sensor curved surface top-center
point was aligned with the ATI nano sensor, and the stage presses the sensors against each
other.
The sensors are aligned visually in such a way that the tactile sensor touches the ATI
nano center. The alignment results from actuating the stage and visually checking
whether they are centered in X and Y directions and touching in the Z direction. After
alignment, the stage moves vertically in steps of 0.05 mm up to 1 mm, pressing the sensors
against each other. The recorded values from the ATI nano and tactile sensor for each step
are plotted in Figure 13a,b, respectively. The load phase corresponding to pressing the
tactile and force sensors against each other is followed by an unload phase, where the
opposite occurs.
The stage position changes every 30 s providing the time frame to record the sensor’s
output. The values presented in Figure 13(d.1–d.4) show the average value for each posi-
tion. The ATI nano output already considers a calibration from the manufacturer to pro-
vide the force and torque output in Figure 13a. The tactile sensor output comprises indi-
vidual voltage measurements of the eight Si chips. We use the percentage of change in
resistance, ΔResistance (%), to quantify the applied magnetic field change (equivalent
field change (μT)).
the data from
the PC to the three servo motors Thorlabs stage controlling the displacement.
The ATI nano 17 is a multi-axis force and torque sensor system that is able to measure
both forces (
Fx
,
Fy
, and
Fz
) and torques (
Tx
,
Ty
, and
Tz
). In this setup we attached one
to an aluminum cantilever fixed to the table. A computer communicates with the stage,
providing instructions to align and press the sensors against each other while reading their
output. This setup configuration allows an evaluation of the sensor performance under
normal and shear forces. For this case, the tactile sensor curved surface top-center point
was aligned with the ATI nano sensor, and the stage presses the sensors against each other.
The sensors are aligned visually in such a way that the tactile sensor touches the ATI
nano center. The alignment results from actuating the stage and visually checking whether
they are centered in X and Y directions and touching in the Z direction. After alignment,
the stage moves vertically in steps of 0.05 mm up to 1 mm, pressing the sensors against each
other. The recorded values from the ATI nano and tactile sensor for each step are plotted in
Figure 13a,b, respectively. The load phase corresponding to pressing the tactile and force
sensors against each other is followed by an unload phase, where the opposite occurs.
Sensors 2021,21, 5098 13 of 23
Sensors 2021, 21, x FOR PEER REVIEW 14 of 25
The percentage of change in resistance is the arithmetic average for a sensor when
the stage is in a position (p) relative to the arithmetic mean of the same sensor for the
initial position of the stage, without any applied force.
Figure 13.
(
a
) Force and momentum data from ATI nano 17 (
Fx
,
Fy
,
Fz
,
Tx
,
Ty
and
Tz
) for the loading and unloading
phases. (
b
) Detail of the sensor with the
Tx
and
Ty
schematics detailed. (
c
) A schematic view of the sensor matrix in the XZ
plane; (
d
) a schematic view of the sensor matrix in the XY plane, showing the sensors; (
d.1
) set of four graphs showing
the symmetry for the sensor output by choosing sensors: (d.2) with positive y coordinates (odd numbers—L1, R1, L3, R3);
(
d.2
) with negative x coordinates (left side—L1, L2, L3, L4) (
d.3
) with negative y coordinates (even numbers—L2, R2, L4,
R4); (d.4) with positive x coordinates (right side—R1, R2, R3, R4).
Sensors 2021,21, 5098 14 of 23
The stage position changes every 30 s providing the time frame to record the sensor’s
output. The values presented in Figure 13(d.1–d.4) show the average value for each
position. The ATI nano output already considers a calibration from the manufacturer to
provide the force and torque output in Figure 13a. The tactile sensor output comprises
individual voltage measurements of the eight Si chips. We use the percentage of change in
resistance,
∆
Resistance (%), to quantify the applied magnetic field change (equivalent field
change (µT)).
The percentage of change in resistance is the arithmetic average for a sensor when the
stage is in a position (p) relative to the arithmetic mean of the same sensor for the initial
position of the stage, without any applied force.
∆Rp(%)=Rpi −Rp=0, i
Rp=0
×100 (1)
Equivalent Field Change (mT)=∆Rp(%)
Sensitivity %
mT (2)
The equivalent field change represents the change in magnetic field aligned with the
sensor plane and perpendicular to the pinned layer and we calculate it using Equation (4).
The change in resistance results from Equation (3), while the value used for sensitivity
is 0.72%/mT. The value of sensitivity is the average of the individual sensor sensitivity
obtained from the slope of the MR(H) curve shown in Figure 8. The Si chips have a
spin-valve sensor series sensitive to magnetic fields in the x-axis and the permanent
magnet aligned with the FPC center. This configuration results in the sensor output of
Figure 13(d.1–d.4)
, where sensors on the left side detect a decrease in a magnetic field (d.2)
while the sensors on the right side show the opposite behavior (d.4).
The values recorded can be divided into three main phases: an initial adjustment
phase (I—blue), a torque-dominated phase (II—green), and a vertical force-dominated
phase (III—orange). The initial adjustment phase (I—blue) occurs in the first 150
µ
m of
vertical stage displacement. In this phase, the forces are below the detection limit of ATI
nano 17 but detectable by the tactile sensor since the tactile sensor matrix measures a
change in a magnetic field coherent with the displacement, both for loading and unloading.
Moreover, the sensor output has a precise symmetry, with positive y coordinates (odd
numbers—L1, R1, L3, R3) and negative y coordinates (even numbers—L2, R2, L4, R4). We
attribute this phase to gaps present in the whole experimental setup system that require
less force than the minimum force the ATI nano can detect.
The second phase (II—green) occurs from 0.15 to 0.4 mm, and we observe a more
significant increase in
Tx
than in
Fz
, thus we have named it a torque-dominated phase.
Torque was not intentionally imposed, but it is consistent with a misalignment during
production or assemblage of the sensor matrix (see Figure 13(b.3)). The tactile sensor’s
output is also consistent with a rotation in the x-axis, which can be observed by comparing
the outputs in Figure 13 d.1 with d.3. In phase II the sensors with positive y-coordinates
(odd numbers—L1, R1, L3, R3) show a positive change, while negative y-coordinates (even
numbers—L2, R2, L4, R4) show no observable output.
The last phase (III—orange) where
Fz
becomes a more significant factor than any other
force or torque, from 0.4 to 1 mm,
Fz
has a similar behavior to the simulated force (Figure 15f).
4.5. Simulating the Experimental Setup
4.5.1. Simulation Assumptions
To improve our understanding of the results and accelerate the development and
optimization of new designs, we represented and simulated the same system in three
dimensions using finite element software, COMSOL. The model only considers the Si chips,
the elastomeric part, and the ATI nano 17 (Figure 14). The FPC, the epoxy glue, and the
finger part are not considered in the model since they would significantly increase the
computational complexity and their contributions are most likely negligible. The main
Sensors 2021,21, 5098 15 of 23
purpose of this simulation is to explore the relationship between the displacement and the
magnetic field detected in the surface of the Si chips (related to the sensor output in the
experimental data). This relationship represents the working principle of the device, thus,
understanding the metrics that have the most impact on the working principle allows us
to develop better-suited devices for the application. Moreover, simulation decreases the
iteration time significantly, thus connecting the experimental data and simulation data is
key for an efficient design and development of these sensors.
Sensors 2021, 21, x FOR PEER REVIEW 16 of 25
to develop better-suited devices for the application. Moreover, simulation decreases the
iteration time significantly, thus connecting the experimental data and simulation data is
key for an efficient design and development of these sensors.
The simulation comprises: (i) a mechanical model, where the bottom of the elasto-
meric part was fixed (blue area in Figure 14(a.3)) and the ATI nano 17 was pushed against
it in steps of 0.05 mm until 0.5 mm and (ii) a magnetic model, where the magnetic field H
(Hx, Hy, Hz) was calculated for each Si chip.
Figure 14. Three-dimensional geometry used for the simulations: (a) XZ plane view; (a.1) YZ plane
view; (a.2) XY plane view; and (a.3) an orthogonal view identifying the initial constraints, the blue
represents the finger part on the Thorlabs stage and thus was fixed, while the green area highlights
the surface that will be pressed against the PDMS part in steps of 0.05 mm up to a maximum of 0.5
mm.
4.5.2. Mechanical Simulation
The mechanical model simulates the deformation of the PDMS part when pressed by
the ATI nano 17, and consequently how the Si chips are displaced.
The most challenging aspect of this simulation was defining the interface behavior
correctly. The interface between the ATI nano and the elastomeric part had to be defined
in such a way that allowed for the area of contact to change as the ATI nano displacement
increased. This was achieved by using the augmented Lagrange pressure method availa-
ble in COMSOL 3.5 to simulate the evolution of the contact area. To simulate the defor-
mations of the PDMS part, an incompressible neo-Hookean hyperelastic material model
was used in COMSOL [33]. The parameters used for the mechanical simulations are de-
tailed in Table 1.
Figure 14.
Three-dimensional geometry used for the simulations: (
a
) XZ plane view; (
a.1
) YZ plane view; (
a.2
) XY plane
view; and (
a.3
) an orthogonal view identifying the initial constraints, the blue represents the finger part on the Thorlabs
stage and thus was fixed, while the green area highlights the surface that will be pressed against the PDMS part in steps of
0.05 mm up to a maximum of 0.5 mm.
The simulation comprises: (i) a mechanical model, where the bottom of the elastomeric
part was fixed (blue area in Figure 14(a.3)) and the ATI nano 17 was pushed against it in
steps of 0.05 mm until 0.5 mm and (ii) a magnetic model, where the magnetic field H (Hx,
Hy, Hz) was calculated for each Si chip.
4.5.2. Mechanical Simulation
The mechanical model simulates the deformation of the PDMS part when pressed by
the ATI nano 17, and consequently how the Si chips are displaced.
Sensors 2021,21, 5098 16 of 23
The most challenging aspect of this simulation was defining the interface behavior
correctly. The interface between the ATI nano and the elastomeric part had to be defined
in such a way that allowed for the area of contact to change as the ATI nano displacement
increased. This was achieved by using the augmented Lagrange pressure method available
in COMSOL 3.5 to simulate the evolution of the contact area. To simulate the deformations
of the PDMS part, an incompressible neo-Hookean hyperelastic material model was used in
COMSOL [
33
]. The parameters used for the mechanical simulations are detailed in Table 1.
Table 1. Parameters used for each material in the mechanical simulation.
Mechanical Properties
Part PDMS ATI Nano 17 Si Chip
Material
PDMS—
Polydimethylsiloxane
(1:15)
Aluminum Silicon (solid, [100] axis)
E 750 kPa 6.91 GPa 13.02 GPa
u 0.49 0.33 0.28
K - 25.98 GPa 79.67 GPa
µ251.68 N/mm2- -
l12.33 kN/mm2- -
E = Young modulus; υ= Poisson ratio; K = Bulk modulus; Laméparameters—µand λ.
In this simulation we forced a vertical and controlled displacement of the aluminum
cylinder to compress the elastomeric part. This displacement forced the deformation of the
elastomeric part and a reaction force on the aluminum part. The total force measured in the
green surface is plotted against the displacement on Figure 15e can be compared with the
experimental data in 13a. The mechanical model seems to describe the experimental data
acceptably, particularly if we consider the phase I 150
µ
m gap discussed in the experimental
data. The simulation was only done for 0.5 mm to make sure we were working in the
elastic domain so the model could have any significance.
4.5.3. Magnetic Simulation
The magnetic simulation is performed for each displacement step because for each
displacement a different position in space results in a different magnetic field. The magni-
tude of the magnetic field generated by the cylindrical NdFeB magnet is simulated and
measured on the active area of the Si chips for each displacement.
For the geometrical configuration of the tactile sensor the magnetic field ranges
between
−
2 and 2 mT on the surface of the Si chips where the sensor is fabricated (colored
surfaces in Figure 16a).
The transfer curve (Figure 8) shows that the sensors respond linearly to magnetic
fields ranging from
−
2.5 to 2.5 mT perpendicular to their easy axis. However, each point
of the colored surfaces (Figure 16a) is a three-dimensional vector of magnetic field with
origin at the permanent magnet. To calculate the field detected by the sensor, and be able
to compare the simulation to the experimental data, we must consider the active area of
the sensors and the angle of the surface with respect to the XY plane.
The active sensor area of 200
×
95
µ
m
2
(see in Figure 4b) in the center of the Si chip
is used for determining the magnetic field in further calculations such as Figure 16c, as
opposed to the average of the colored surface as a whole. In the same figure we can see a
linear relationship between the magnetic field and the displacement. Moreover, symmetry
between the position of the sensors in the matrix and the simulated magnetic fields is
expected, as sensors on the left side (negative x-coordinates) show a negative Hx and
sensors with a positive y-coordinate show a positive Hy.
Sensors 2021,21, 5098 17 of 23
Sensors 2021, 21, x FOR PEER REVIEW 18 of 25
Figure 15. Results of the three-dimensional mechanical simulation detailing the XY plane and ZX plane and showing the
displacement results for the steps in color: (a) 0.05 mm; (b) 0.25 mm; and (c) 0.5 mm. (d) XZ plane showing the position
and displacement of the Si chips. (d.1) Detail of the matrix displacement for each pressing step. (e) The force value, ob-
tained by integrating the value of force in each point of the green surface on Figure 14(a.3).
Figure 15.
Results of the three-dimensional mechanical simulation detailing the XY plane and ZX plane and showing the
displacement results for the steps in color: (
a
) 0.05 mm; (
b
) 0.25 mm; and (
c
) 0.5 mm. (
d
) XZ plane showing the position and
displacement of the Si chips. (
d.1
) Detail of the matrix displacement for each pressing step. (
e
) The force value, obtained by
integrating the value of force in each point of the green surface on Figure 14(a.3).
Sensors 2021,21, 5098 18 of 23
1
Figure 16.
Three-dimensional magnetic simulation, showing (
a
) the Si chip and the field magnitude in Hx, Hy, and Hz
magnitudes on the surface where the sensors for d = 0 and 0.5 mm. (
b
) XZ plane showing the position and displacement
of the Si chips, the position of the magnet, and the magnetic field lines. (
b.1
) Detail of the matrix displacement for each
pressing step. (
c
) Average magnetic field magnitude calculation in each Si chip in their active sensor area (200
×
95
µ
m
2
) for
each component in µT: (c.1)Hx(c.2)Hyand (c.3)Hz.
Sensors 2021,21, 5098 19 of 23
4.5.4. Sensor Tilting
In addition to the sensor’s active area position in space we must consider the rotation
angle of the Si chip surface with respect to the permanent magnet referential in three
dimensions
(α,β,γ)
. The Si chip surface plane with the XY plane is referred as
α
. The
green referential frame represents the magnetic simulation system of coordinates (x,y,z),
with origin at the center of the magnet. The blue referential (a,b,c) has its origin in the
center of the sensor and is defined with
→
ea
and
→
eb
being orthogonal and lying in the
sensor plane and
→
ea
aligned with the sensor sensitive direction. Because the sensor is only
sensitive in one direction on the plane and the field is a vector in the three dimensions,
taking the angle (
α
,
β
,
γ)
of the Si chip with the permanent magnet is needed to evaluate
the magnitude of the sensor output, henceforth
→
Hsensor
. To determine its value, we use
the following: →
Hsensor =Rx(α)Ry(β)Rz(γ)
→
H(3)
Ha
Hb
Hc
=
1 0 0
0 cos α−sin α
0 sin αcos α
cos β0 sin β
0 1 0
−sin β0 cos β
cos γ−sin γ0
sin γcos γ0
0 0 1
Hx
Hy
Hz
(4)
The
α
values obtained in the simulation for each Si chip are shown in Figure 17b.
For this case, the angles
β
and
γ
are considered null as no change was observed in this
simulation. This is attributed mainly to the pressing object flat geometry that has an area
larger than the sensor as well as the applied force only has a vertical component. Smaller
or curvilinear pressing objects would have a significant impact on β, while either torques,
shear forces, or both, would impact γ.
To estimate the
→
Hsensor (Ha,Hb,Hc)
we use Equation (3), where
→
HHx,Hy,Hz
is the
magnetic field vector for the coordinates in 3D space corresponding to the center of the
sensor’s active area. The matrices in (4) are used to obtain
→
Hin the abc referential.
The ratio between
Ha
and
Hb
is 2:1 and the fact that
Hb
<< 1 mT, suggest that
Hb
influence due to crossfield phenomena on the sensor output can be discarded. The values
of
Ha
against the displacement are shown in Figure 17c. With these calculations we
estimate
Ha
range between
−
0.7 and 0.7 mT, which is well within the linear range of the
sensor-transfer curves presented in Figure 8.
4.6. Simulation and Experimental Data, How Do They Compare?
Section 4.4 discusses the three phases of the physical experiment and how the change
in resistance value is obtained. This means that a change of 0.05% in the signal of the sensor
should correspond to a 50
µ
T change. In order to compare this with the simulation results,
we need: (1) only use the data from phase III of experimental data; and (2) to use the same
principles for the simulation data, meaning we need to look at changes in magnetic field.
The first part implies that we plot the change of resistance discarding phases I and
II and assume the
Rp=0
in Equation (3) to be
p=
0.4 instead of
p=
0 (Figure 18a). As
for the simulated data, we use
Ha
and the field for the position to be clearly visualize the
similarities between both sets of data; Figure 18b shows the variation of the
Ha
as a function
of displacement for each sensor. This can be compared directly with the equivalent field
change (
µ
T). The simulation seems to predict the experiment accurately for the sensors L2,
R1, R2, and R4.
Sensors 2021,21, 5098 20 of 23
Sensors 2021, 21, x FOR PEER REVIEW 22 of 25
Figure 17. (a) XZ plane schematics showing the relationship between the Si chip (blue) and permanent magnet (green)
reference frame; (b) angle simulated and taken from the mechanical simulation for each Si chip, in this case both
and are 0. (c) abc referential centered on the spin valve active area, which coincides with the Si chip’s surface. (d) The
magnetic field magnitude on the active area of the sensor.
4.6. Simulation and Experimental Data, How Do They Compare?
Section 4.4 discusses the three phases of the physical experiment and how the change
in resistance value is obtained. This means that a change of 0.05% in the signal of the sen-
sor should correspond to a 50 μT change. In order to compare this with the simulation
results, we need: (1) only use the data from phase III of experimental data; and (2) to use
the same principles for the simulation data, meaning we need to look at changes in mag-
netic field.
The first part implies that we plot the change of resistance discarding phases I and II
and assume the
in Equation (3) to be=0.4 instead of =0 (Figure 18a). As for
the simulated data, we use and the field for the position to be clearly visualize the
similarities between both sets of data; Figure 18b shows the variation of the as a func-
tion of displacement for each sensor. This can be compared directly with the equivalent
field change (μT). The simulation seems to predict the experiment accurately for the sen-
sors L2, R1, R2, and R4.
Figure 17.
(
a
) XZ plane schematics showing the relationship between the Si chip (blue) and permanent magnet (green)
reference frame; (
b
) angle
α
simulated and taken from the mechanical simulation for each Si chip, in this case both
β
and
γ
are 0. (
c
) abc referential centered on the spin valve active area, which coincides with the Si chip’s surface. (
d
) The magnetic
field magnitude on the active area of the sensor.
Sensors 2021, 21, x FOR PEER REVIEW 23 of 25
Figure 18. (a) Experimental results: sensor output as a function of displacement for phase III. (b) Simulated results consid-
ering change in angle.
5. Conclusions
We presented an innovative design for a tactile sensor integrated into the fingertip of
a robotic hand, based on magnetic sensing, and leveraging a working principle that was
not attempted in the literature so far. The main advantage of this working principle is the
ability to pin-point the exact source of displacement by measuring the displacement of the
sensors on the surface of the finger. This was possible thanks to a comprehensive simula-
tion of the behavior of the different components of the sensor, that allowed optimizing of
the design of the system and to obtain the desired performance, and an effective minia-
turization of the physical components.
The influence of external fields is a concern for this application. We believe there are
two main strategies to mitigate errors from this source. The first is to add a magnetic shield
between the sensors and the external field, which would mitigate the influence of external
magnetic fields. Magnetic shields can be made from materials with high permeability that
can be used to manipulate magnetic field lines either of magnetic flux concentrators to
amplify small magnetic signals [34], or the opposite. Mu metal (nickel-iron alloy) is a com-
mon material used in magnetic shielded cameras for low noise measurement.
The other is to use algorithms to treat the information of the sensors and this can
determine the probability that the change in magnetic field was caused by an external
source or due to a deformation of the elastomer. Knowing the characteristics of the cylin-
drical permanent magnet and expected position of each sensor, allows us to have a rea-
sonable expectation regarding the flexible sensing response. In cases where this does not
match, it is highly likely that an external magnetic field is present. In a situation where all
the sensors detect the same change in magnetic field (i.e., earth magnetic field) would
result in an offset, for all the sensors simultaneously. A constant offset in the same mag-
nitude and direction for all the sensors is not the expected behavior when a deformation
occurs (as discussed in Section 4.4). The symmetry of the magnetic field from the cylindri-
cal permanent would allow us to filter it. An example where such a strategy is successfully
used is when minimizing magnetic noise for magnetoencephalography (MEG) equip-
ment, where advanced mathematical algorithms applied on the multichannel MEG data
are used to minimize external field interference [35].
The number of sensors is a critical parameter for this design. There are two reasons
for increasing the number of sensors: improved ability to identify sources of external fields
and better ability to detect the contact point.
Figure 18.
(
a
) Experimental results: sensor output as a function of displacement for phase III. (
b
) Simulated results
considering change in angle.
Sensors 2021,21, 5098 21 of 23
5. Conclusions
We presented an innovative design for a tactile sensor integrated into the fingertip of a
robotic hand, based on magnetic sensing, and leveraging a working principle that was not
attempted in the literature so far. The main advantage of this working principle is the ability
to pin-point the exact source of displacement by measuring the displacement of the sensors
on the surface of the finger. This was possible thanks to a comprehensive simulation of the
behavior of the different components of the sensor, that allowed optimizing of the design
of the system and to obtain the desired performance, and an effective miniaturization of
the physical components.
The influence of external fields is a concern for this application. We believe there are
two main strategies to mitigate errors from this source. The first is to add a magnetic shield
between the sensors and the external field, which would mitigate the influence of external
magnetic fields. Magnetic shields can be made from materials with high permeability
that can be used to manipulate magnetic field lines either of magnetic flux concentrators
to amplify small magnetic signals [
34
], or the opposite. Mu metal (nickel-iron alloy) is a
common material used in magnetic shielded cameras for low noise measurement.
The other is to use algorithms to treat the information of the sensors and this can
determine the probability that the change in magnetic field was caused by an external
source or due to a deformation of the elastomer. Knowing the characteristics of the
cylindrical permanent magnet and expected position of each sensor, allows us to have a
reasonable expectation regarding the flexible sensing response. In cases where this does not
match, it is highly likely that an external magnetic field is present. In a situation where all
the sensors detect the same change in magnetic field (i.e., earth magnetic field) would result
in an offset, for all the sensors simultaneously. A constant offset in the same magnitude
and direction for all the sensors is not the expected behavior when a deformation occurs
(as discussed in Section 4.4). The symmetry of the magnetic field from the cylindrical
permanent would allow us to filter it. An example where such a strategy is successfully
used is when minimizing magnetic noise for magnetoencephalography (MEG) equipment,
where advanced mathematical algorithms applied on the multichannel MEG data are used
to minimize external field interference [35].
The number of sensors is a critical parameter for this design. There are two reasons
for increasing the number of sensors: improved ability to identify sources of external fields
and better ability to detect the contact point.
Author Contributions:
Conceptualization, M.N.; methodology, M.N., P.R., S.C.; validation, L.J., A.B.
and R.N.; formal analysis, M.N., S.C.; investigation, M.N., P.R., R.N.; resources, M.N.; data curation,
M.N.; writing—original draft preparation, M.N.; writing—review and editing, M.N., L.J., A.B., S.C.;
visualization, M.N.; supervision, A.B., S.C.; project administration, R.N.; funding acquisition, L.J.,
S.C. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was partially supported by Fundação para a Ciência e Tecnologia through
Project MagScopy4IHC-LISBOA-01-0145-FEDER-031200 and the National Infrastructure Roadmap
Unit (UID/05367/2020) through pluriannual BASE, PROGRAMATICO financing. M. Neto and
P. Ribeiro acknowledge FCT for PhD grants, PD/BD/105933/2014 and SFRH/BD/130384/2017,
respectively. We acknowledge partial support from MagID project co-financed by European Com-
mission Directorate General for Research & Development as part of the Horizon 2020 - research and
innovation framework programme Fast track to Innovation under the Project ID: EIC FTI GA 870017.
Work partially supported by the EPSRC UK through projects NCNR (EP/R02572X/1) and MAN3
(EP/S00453X/1).
Conflicts of Interest: The authors declare no conflict of interest.
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