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Frequenz 2018; 72(3-4): 93–99
Laven Mavarani∗, Philipp Hillger, Thomas Bücher, Janusz Grzyb, Ullrich R. Pfeiffer,
Quentin Cassar, Amel Al-Ibadi, Thomas Zimmer, Jean-Paul Guillet, Patrick Mounaix
and Gaëtan MacGrogan
NearSense – Advances Towards a Silicon-Based
Terahertz Near-Field Imaging Sensor for Ex Vivo
Breast Tumour Identification
https://doi.org/10.1515/freq-2018-0016
Received January 9, 2018
Abstract: Breast Cancer is one of the most frequently dia-
gnosed cancer diseases worldwide, and the most common
invasive tumour for women. As with all cancers, early
detection plays a major role in reducing the mortality and
morbidity rate. Currently, most breast cancers are detec-
ted due to clinical symptoms, or by screening mammo-
graphy. The limitations of these techniques have resulted
in research of alternative methods for imaging and detect-
ing breast cancer. Apart from this, it is essential to define
precise tumour margins during breast-conserving surger-
ies to reduce the re-excision rate. This study presents the
advances in the development of a silicon-based THz sub-
wavelength imager usable in life science applications,
especially for tumour margin identification.
Keywords: tumor margin identification, terahertz waves,
medical imaging, near-field sensor, silicon technology
1 Introduction
Cancer is one of the leading causes of morbidity and mor-
tality worldwide, with approximately 14 million new cases
reported in 2012 [1]. The number of new cases per year is
expected to rise by about 70% over the next two decades.
*Corresponding author: Laven Mavarani, Institute for
High-Frequency, and Communication Technology, University of
Wuppertal, 42119 Wuppertal, Germany,
E-mail: mavarani@uni-wuppertal.de
Philipp Hillger, Thomas Bücher, Janusz Grzyb, Ullrich R. Pfeiffer,
Institute for High-Frequency, and Communication Technology,
University of Wuppertal, 42119 Wuppertal, Germany
Quentin Cassar, Amel Al-Ibadi, Thomas Zimmer, Jean-Paul Guillet,
Patrick Mounaix, IMS UMR CNRS 5218, University of Bordeaux,
33400 Talence, France, E-mail: quentin.cassar@ubordeaux.fr
Gaëtan MacGrogan, Institute Bergonié, Centre Régional de Lutte
Contre le Cancer, 33076 Bordeaux, France,
E-mail: G.MacGrogan@bordeaux.unicancer.fr
Breast cancer (Mamma Carcinoma; BC) is considered the
most common cancer in women. Currently, the average
risk of a woman in the United States developing breast
cancer sometime in her life is about 12% [2] Countries
where industrialization is a more recent phenomenon
have a rising incidence and higher mortality [3]. The com-
monly used screening method is a combination of clin-
ical examination, mammography and ultrasound. These
methods provide a good indication whether a lump is can-
cerous or not. Mammography is one of the most effective
detection techniques so far, but it still has a low sens-
itivity and exposes the patient to ionizing radiation [4].
If a lump is regarded as cancerous, needle biopsies
are performed to determine further information about
the genetic origin and grade of the cancer. Breast con-
serving surgeries (also called lumpectomy) are performed
to remove the diseased tissue. Removed lymph nodes are
examined for remaining tumour cells in the margin tissue.
If there are still cancer cells present, a second operation
must be performed. Therefore, non-ionizing techniques
that offer tumour margin identification with high pre-
cision are highly in demand to reduce the re-excision
rate [5].
The use of terahertz (THz) technology for life-science
applications has recently gained a lot of attention. Fre-
quency dependent absorption lines of liquids and solids
have been measured in the past and have shown charac-
teristic spectral fingerprints in the THz region, that make
it even possible to gain insights on the bio-molecular
level [6, 7]. In the past, studies have demonstrated that
THz applications can determine the water, sucrose, alco-
hol, liquid fuel, and petroleum content [8, 9]. Even collect-
ive mode vibrations have been identified for alcohols [10].
Other recent studies have shown that the THz techno-
logy makes it possible to quantify the way that water
interacts with biomolecules, which enables the study
of molecular hydration [11]. THz is non-ionizing, and
can be used without hazards for medical and biological
samples [12]. As an example, terahertz time-domain spec-
troscopy (THz-TDS) has opened many new opportunities
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94 L. Mavarani et al.: Silicon-Based Terahertz Near-Field Imaging Sensor
in the field of medicine and biology [13]. Studies on breast
tumour margins have shown successfully that they are
able to distinguish between healthy and diseased tis-
sue [14, 15]. However, the broad utilization of spectro-
scopic terahertz methods has been held back by the lack
of low-cost and compact sensing systems, and the dif-
fraction limitation of terahertz waves for superresolution
imaging [16]. In order to overcome the diffraction limit
in the THz region, near-field scanning optical microscopy
(NSOM) is widely used [17], making it possible to achieve
resolutions down to 20-40 nm using atomic force micro-
scopy tips [18]. However, NSOM has the disadvantage of
low integration with weak detection signals, or high integ-
ration times, and therefore cannot be used for real-time
super resolution imaging [19]. On the other hand, THz
near-field sensors based on silicon technology have been
significantly improved recently, especially when com-
pared to NSOM with regards to sensor sensitivity, system
cost, and scanning time [20, 21].
Previously, a silicon-integrated super-resolution
near-field sensor with dielectric permittivity-based
imaging contrast was presented [20, 21]. The scientific
breakthrough is provided by a fully-integrated THz
near-field sensor pixel comprising of a THz source (trans-
mitter), an electromagnetic near-field sensor element
(transducer), and a THz detector (receiver) including its
readout. The THz near-field sensor measures the complex
dielectric permittivity, and thereby enables the exploita-
tion of the benefits of terahertz radiation with the required
sub-wavelength optical resolution for intraoperative bio
imaging. This near-field sensor is based on a commer-
cially available 0.13 µm SiGe-HBT (silicon-germanium
heterojunction bipolar transistors) technology and exhib-
its a lateral resolution that reaches down to 10 µm.
Moreover, the architecture of the sensor is compact and
scalable, allowing integration of large arrays for scan-
ning time reduction [22]. For this study, the sensor was
modified to enable a chopping technique for flicker noise
suppression. This results in a highly-improved signal-
to-noise-ratio (SNR) that enables the detection of small
differences in permittivity, which is a key requirement for
the study of medical or biological samples.
In parallel to the development of the near-field
sensor, investigations regarding tissue response and ima-
ging contrast of freshly excised breast tissue were per-
formed in the region of 300–600 GHz using THz Time-
Domain-Spectroscopy (TDS). These studies demonstrate
that this frequency range provides sufficient contrast
between healthy and malignant breast tissues and is
well-suited to be used in a fully integrated near-field
imaging sensor. In addition, the resolution in near-field
measurements provides a resolution closer to the typical
eukaryote cell diameter, in contrast to the resolution of
regular THz TDS, and thus might enable better discrimin-
ation between margins. The knowledge of these frequen-
cies, combined with single-pixel near-field sensors, could
be used for the development of a multi-pixel near-field
imager for life-science applications [23].
2 Experimental setup and results
The present study is divided into two parts: The first
part investigates the performance of THz radiation in the
300–600 GHz range to evaluate the potential of spectral
terahertz imaging to discriminate healthy from malignant
breast tissues. In the second part advances in creating
a fully-integrated 0.53 THz near-field sensor, implemen-
ted in 0.13 µm SiGe HBT technology for the detection of
small changes in dielectric permittivity based contrast,
are shown.
2.1 THz TDS
A THz TDS spectroscopy system was implemented by the
University of Bordeaux directly at the hospital (Institut
Bergonié). There, 17 freshly excised breast tissues with
different cancer types and grades have been collected,
measured, and analyzed just after the excision in the oper-
ating room. Complementary, automatic signal and data
processing has been developed, based on different stat-
istical methods to explore their feasibility to provide the
optimum contrast between benign and malignant breast
tissues [23], whilst helping with the low data display com-
plexity level required to transfer such a technique to a
hospital.
In Figure 1 two exemplary BC tissue samples are
shown. Each of the two samples has been measured at
different frequencies between 310–630 GHz in order to
determine the frequency associated with the highest res-
olution and contrast. The images show that the higher
frequencies between 490–630 GHz reflect the best resol-
ution, when compared to the tissue structures.
In Figure 2 the same BC tissue sample as in Figure 1
(upper row) is depicted. This time the sample has been
measured with THz TDS at 300, 400, 500 and 600 GHz,
respectively. On the left side the H&E (Hematoxylin &
Eosin) stained histology section of the breast cancer tis-
sue is shown. The adipose tissue is washed out during
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L. Mavarani et al.: Silicon-Based Terahertz Near-Field Imaging Sensor 95
Figure 1: THz-images of BC tissue sections from 310-630 GHz. Higher frequencies show a better resolution in the THz-image compared
to the tissue structures measured.
Figure 2: Comparison between the H&E stained histology section of a malignant breast tissue (upper row left) and the corresponding
THz-images of same tissue section before deparaffinization at 300, 400, 500 and 600 GHz, respectively (from left to right). The color bar
reflects the relative intensities of the THz images.
paraffinization process, and hence is not apparent in the
histology slide. The THz-images here relate the reflected
signal amplitude in dependence of the refractive index.
Features of interest are highlighted with dashed lines
within the tissue and can be compared with the THz TDS
images. Frequencies ranging from 300 to 400 GHz exhibit
interesting demarcations between cancerous and healthy
regions. However, all malignant sectors are not well delin-
eated. For frequencies higher than 600 GHz the contrast
is lower, and discrimination is tedious. The contrast loss
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96 L. Mavarani et al.: Silicon-Based Terahertz Near-Field Imaging Sensor
for a specific tissue location over the selected working
frequency band is mostly induced by the decrease of the
signal-to-noise-ratio (SNR) in THz TDS when going to high
frequencies [24].
PCA and usual signal processing have been per-
formed, and the results show that the provided differences
regarding the reflective index between BC and normal
tissue in the region from 300–600 GHz are similar. The
differences are small, but sufficient for discrimination of
different tissue types when combined with the higher res-
olution between 500–600 GHz, and may therefore help
for intraoperative breast tumour margin detection. These
insights open the way for a silicon-based terahertz sub-
wavelength imager design, efficient up to 600 GHz to
address ex vivo life science applications [24].
2.2 Near-field THz sensor
In this present work, a modified version of the fully-
integrated near-field single ended sensor pixel previ-
ously presented in [21] is shown. This solid-state super-
resolution imaging device in 0.13 µm SiGe-HBT techno-
logy operates around 534–562 GHz and is fully integrated
with a complete imaging functionality, including a tun-
able continuous wave (CW) illumination source, near-
field sensing, and power detection. The heart of the device
is a cross-bridged double split-ring resonator (SRR) that
features a 3-D topography to achieve high-spatial con-
finement of the surface near-fields, and is capable of
resolving structural details with an estimated lateral res-
olution down to 10–12 µm. Moreover, the modified sensor
pixel device enables chopping of the 0.55 THz oscillator
for detector flicker noise suppression. Figure 3 shows the
micrograph of the near-field sensor used in this work.
In on-wafer SNR measurements the sensor SNR,
defined by the maximum current response to a metallic
object divided by the spot-noise, reaches up to 115 dB
at a chopping frequency of 25 kHz, being significantly
higher than the previously reported 42 dB SNR for the DC-
operated sensor (Figure 3). In this way, the sensor can
detect even small differences in the permittivity, and thus
is capable of a better contrast generation in biological
samples.
The first experiments were performed to show that
this single ended near-field sensor pixel is capable of
imaging. For this purpose, a commercially available STM
(Scanning Tunneling Microscope) setup (Semilab Nav-
igator 220) was modified, which allows the sensor to
be scanned along an object (Figure 4). Therefore, the
Figure 3: A: Simplified schematic and micrograph of the near-field
single-ended sensor with the cross-bridged double split-ring
resonator (SRR) and chopping. B: SNR measurement of the sensor
defined by the maximum current response to a metallic object
divided by the spot-noise at the chopping frequency.
sensor is mounted on a z-moveable holder. Using a high-
resolution digital CCD camera from below, the sensor
is monitored manually for planarization. The sample of
interest is subsequently fixed on a holder mounted on a
high precision x,y piezo table. For these measurements,
the sensor output current was detected with a chopping
frequency of 30 kHz.
Imaging results are shown in Figure 5, where the single
pixel near-field imager was kept in close proximity to a
nickel- (Ni-) mesh without making contact. The measured
mesh has a 50 µm bar width and a 250 µm bar pitch (Veco
Specimen Grid 0100-NI). The 2D image was acquired dur-
ing a x,y-scan over an area of 60×1000 µm with a step-size
of 1 pixel per 10 µm. The scanning time per pixel was set at
8 seconds. The false colour image reflects the structure of
the inner mesh. In another measurement, a 200×200 µm
scan of a single bar was imaged more precisely. Here,
the resolution was selected to 20×20 pixel with a scan-
ning time of 10 seconds per pixel. The thickness of the bar
according to the false colour image lies at around 60 µm,
which is slightly larger than the actual bar size of the
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L. Mavarani et al.: Silicon-Based Terahertz Near-Field Imaging Sensor 97
Figure 4: Modified STM set-up for near-field sensor scanning experiments. Lower part: Schematic of the set-up showing the sensor
attached to a z mount and the sample fixed on a x,y table.
mesh, and a result of the chosen step-size of 1 pixel per
10 µm. Since these are the results of the first experiments
using the described scanning microscope set-up, neither
the chosen step-size, nor the scanning time per pixel are
optimized. As such, there is still considerable additional
scope for the further improvements regarding the set-up
and measurement parameters.
3 Discussion and conclusion
The set-up using a reflection geometry spectro-THz-
imaging system at the Department of Pathology of the
Bergoniè Institute has resulted in the measurement
of seventeen freshly excised breast tissue samples of
different cancer types and grades. The analysed results
indicate that THz-imaging in the area from 300–600 GHz
holds significant potential for the discrimination of fatty
tissue from the cancer–fibre matrix, and- more important-
also shows differences between cancer and normal tissue.
Various data and signal processing techniques have also
been utilized to enhance the contrast between tissue kinds
from raw frequency data. Statistical approaches, such
as FD (frequency domain) FWMH (full-width half max-
imum) and TD (time domain) mean pixel signal over the
frequency band, have yielded promising results, bring-
ing additional information on tissue constitution [24].
For more in-depth investigations further tissue samples
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98 L. Mavarani et al.: Silicon-Based Terahertz Near-Field Imaging Sensor
Figure 5: False colour imaging using the single pixel near-field
imager for a Ni-mesh Veco Specimen Grid 0100-NI. The location of
image A and B are indicated in the microscopic image of the grid. A:
60x1000 µm, 3x100 px, time/px=8 s; B: 200x200 µm, 20x20 px,
time/px=10 s; C: Cross section through the grid bar in B indicated
by a white line.
must be measured and analysed. However, the distinc-
tion between fibres and cancerous regions has to be
improved, but is challenging due to the fast degradation
of fresh tissue samples, the low number of available tissue
samples, and the poor resolution of THz TDS in the 300–
600 GHz range. To overcome these resolution limitation,
the near-field sensor described in this study constitutes a
promising option, especially in the frequency range from
500–600 GHz. The approach detailed here makes use of
advanced silicon process technologies, and may provide
the required technology to enable marker-free intraoper-
ative tumour margin identification with terahertz waves.
Thereby, the initial challenges were to develop a compact
sensor with the appropriate sensitivity and a well-defined
sensing area. Since the source, sensor and detector are
located in the same plane, the entire circuitry is located
under the chip top surface. In this way, the sample-sensor
interaction that happens at the chip top surface is not
effected by the circuitry. In [20] we successfully real-
ized a sensor device with these properties. The presented
single-ended near-field sensor has further confirmed that
a fully integrated single-ended near-field sensor pixel is
suited for imaging applications. In a scanning microscope
set-up, it was possible to show a scanned false colour
image of various areas of a Ni-Mesh using the dielectric
permittivity differences between areas of the mesh and
areas of the glass plane below it. Although the bars of
the mesh were successfully imaged, the extracted width
differs from the true value of 50 µm due to the selected
step-size. Instead, the results reveal a bar width of 60 µm.
To improve the near-field sensor imaging, the scan pro-
cedure must be optimized regarding scanning time, which
can be significantly reduced up to few milliseconds, and
step-size, which is not yet adapted to the possible limit to
achieve the best resolution. Scanning time reduction can
additionally be addressed by taking advantage of the scal-
able circuit architecture for array implementation [22]. The
major challenges that remain are to identify the relevant
bio imaging parameters for tumour margin identification
in tissue sample tests. The microscope set-up will be used
for future scans of deparaffinized tissue sections, to build
up a data base of the relevant differences between the tis-
sue components. A problem remains to be solved: The
sensor device needs to be contacted from the backside
to enable free movement over large areas. This can be
achieved by Silicon-Through-VIAs [25]. The results shown
here are a further step towards the goals of the NearSense
project, which will open up many new fields of near-field
THz imaging in life science applications in future, but
especially aims at the use of this sensor device in hospit-
als on freshly excised samples, which in a next step can be
compared to the measurements performed with the THz
TDS set-up.
Funding: This work is part of the project NearSense- A
silicon-based terahertz near-field imaging array for ex vivo
life-science applications and was funded in the frame of
the DFG priority program SPP 1857 ESSENCE (Elektromag-
netic Sensors for Life Sciences).
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