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Single-trap phenomena in nanoscale biosensors. (a) Schematic illustration of a liquid-gated Si NW FET with a single trap that induces (b) two-level RTS fluctuations of the drain current. (c) Schematic interpretation of SR: an optimal amount of white noise is added to a system to detect weak signals under the system threshold. (d) DP noise suppression due to single-trap phenomena considering a single trap as a nonlinear bistable system that can amplify the signal in the regime of SR. (e) Trap occupancy probability (g-factor) and its derivative plotted as a function of gate voltage for simulated RTS noise. (f) Schematic illustration of the conversion of RTS voltage fluctuations into the fluctuations of trap occupancy probability (g-factor noise).

Single-trap phenomena in nanoscale biosensors. (a) Schematic illustration of a liquid-gated Si NW FET with a single trap that induces (b) two-level RTS fluctuations of the drain current. (c) Schematic interpretation of SR: an optimal amount of white noise is added to a system to detect weak signals under the system threshold. (d) DP noise suppression due to single-trap phenomena considering a single trap as a nonlinear bistable system that can amplify the signal in the regime of SR. (e) Trap occupancy probability (g-factor) and its derivative plotted as a function of gate voltage for simulated RTS noise. (f) Schematic illustration of the conversion of RTS voltage fluctuations into the fluctuations of trap occupancy probability (g-factor noise).

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Transistor biosensors are mass-fabrication-compatible devices of interest for point of care diagnosis as well as molecular interaction studies. While the actual transistor gates in processors reach the sub-10 nm range for optimum integration and power consumption, studies on design rules for the signal-to-noise ratio (S/N) optimization in transisto...

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... phenomena as a stochastic resonance effect. The third noise suppression effect, as introduced in 21,22 , aims to exploit the presence of a single active trap in a gate dielectric layer of a nanotransistor, where RTS noise is observed (see Fig. 3a,b). Such an RTS effect is usually avoided as it increases the noise level (see Fig. 2b,c), but if RTS parameters (i.e. trap occupancy probability, time constants) are monitored (see Fig. 3b), then RTS noise becomes a signal. Intuitively, one could expect that the use of RTS as a signal would provide a gain corresponding to the difference ...
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... as introduced in 21,22 , aims to exploit the presence of a single active trap in a gate dielectric layer of a nanotransistor, where RTS noise is observed (see Fig. 3a,b). Such an RTS effect is usually avoided as it increases the noise level (see Fig. 2b,c), but if RTS parameters (i.e. trap occupancy probability, time constants) are monitored (see Fig. 3b), then RTS noise becomes a signal. Intuitively, one could expect that the use of RTS as a signal would provide a gain corresponding to the difference between a single-trap and a trap-free device, e.g. between one and two orders of magnitude (see Fig. 2a,b). Below, we show that the potential of single-trap phenomena for the noise ...
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... that the potential of single-trap phenomena for the noise suppression is even larger and that it is similar to the SR effect observed in biology 31 , enabling here to overpass the thermal DP noise limit. The idea beyond this is that the addition of white noise to a signal that is nonmeasurable below a given threshold can become measurable (see Fig. 3c). As RTS is nothing but a white noise below a cut-off frequency that is added to the signal of interest, there are obviously some similarities (Fig. 3d). However, a technical difference comes from the fact that RTS time constants are related to the signal of interest (surface potential), which is usually not the case for the white ...
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... , enabling here to overpass the thermal DP noise limit. The idea beyond this is that the addition of white noise to a signal that is nonmeasurable below a given threshold can become measurable (see Fig. 3c). As RTS is nothing but a white noise below a cut-off frequency that is added to the signal of interest, there are obviously some similarities (Fig. 3d). However, a technical difference comes from the fact that RTS time constants are related to the signal of interest (surface potential), which is usually not the case for the white noise. Below, we combine theory and experiments to push the limits of noise suppression with single-trap ...
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... this section, the aim is to propose a theoretical framework for the signal-to-noise ratio in the case of the singletrap phenomena approach. We consider the trap occupancy probability as the signal and evaluate the noise of g to determine the S/N ratio (see Fig. 3e,f). We demonstrate experimentally, numerically, and analytically that under optimized conditions, the S/N ratio can be beyond that of the thermal noise in trap-free ...
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... and current fluctuations are noise. In contrast, we define the signal in singletrap-based biosensors 21,22,32 as trap occupancy probability g . To calculate the g-factor noise (fluctuations in time) considering two-level RTS time trace, one can extract g(t) over a given window directly from the distribution of the voltage fluctuations (see Fig. 3f). Then, by sliding the window along with the RTS time trace one can obtain a new time trace with the trap occupancy factor fluctuations in time. The time-domain g-factor data can be then translated into frequency spectrum resulting in the power spectral density S g . experimental results. Figure 4a shows the two-level drain current ...
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... can be seen from Fig. 4c, the g-factor noise decreases with increasing the time window . The dependence of g-factor noise against the time window can be explained by considering the fact that the larger time window contains more transition events enabling g-factor to be estimated with higher accuracy, as illustrated in Fig. 3f. g-factor noise analytical model. Let's consider a two-level RTS signal X t that jumps between states 0 and 1. The transition probabilities P for an RTS with states (0, 1) and rates (, µ) to jump from states 0 to 1, and 1 to 0, respectively, are given by Kolmogorov´s forward equation: www.nature.com/scientificreports/ Then, we consider ...
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... ω = 2πf and is a duration of a sliding time window (see Fig. 3(f)). input-referred g-factor noise S gg . To compare the performance and efficiency of the nanotransistor sensors exploiting single-trap phenomena, one should first introduce and calculate an equivalent input-referred noise caused by the variation of the g-factor. This can be done similarly as for the voltage noise (see Eq. (1b)) defining ...

Citations

... We have used lightly doped thin device layer SOI wafers to demonstrate their suitability for detecting small changes in charge at the electrolyte-oxide surfaces (i.e., caused by the interaction of the proteins with peptides immobilized on the gate surface, which is the long-term goal of this work). A larger planar surface area allows a better signal-to-noise ratio and also less stringent requirements to counter reliability issues, e.g., from pin-holes, as compared to the nanowire counterparts [21]. Figure 1 shows the schematic of the proposed device design ( Figure 1A) and the cross-sectional view of the device layout ( Figure 1B). ...
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... A time trace of the 4-nanowire one and its amplitude evolution versus Vds and Vg are shown in Figure 2. The relative amplitude was as high as 15% of the overall current (Ids vs. Vg in the inset of Figure 3a), i.e., as high as 60% of the current flowing in the nanowire holding the defect. This RTS amplitude was extremely large when compared to 6 the RTS usually obtained in nanotransistors 33,34 . The evolution of the time spent in the up state (up) or down state (down) showed no correlation with Vg ( Figure S5) but demonstrated a clear ...
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... The advancement of semiconductor technology has led to mass production of low cost sensors with advantages such as real-time detection [115], high sensitivity at low concentrations [116], portable and convenient read out circuitry [117]. Over the time researches have experimentally demonstrated various architectures of chemically sensitive FETs for sensing of multiple analytes with considerable improvement in figure of merits (FOMs) for sensors such as sensitivity, linearity, limit of detection (LOD) and signal to noise ratio (SNR) [118,119]. ...
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The interest in biologically sensitive feld-effect transistors (BioFETs) is flourishing explosively due to their potential as biosensors in biomedical, environmental monitoring, and security applications. Recently, the adoption of silicon nanowires in BioFETs has enabled the enhancement of sensing fgure of merits, and device miniaturization. However with the advent of nanoscale BioFETs, reliability issues due to difculty in controlling the fabrication parameters at nanoscale dimensions hamper the sensing performance. Recently, junctionless (JL) approach has been incorporated in feld effect transistors to overcome the fabrication complexities where, current is governed by bulk conduction process. The absence of steep doping profles in JL transistors eases the fabrication complexities, reduces device variability and thermal budget. Imperatives of the performance requirements for next generation biosensors to detect the target chemical and biological molecules with higher sensitivity, small response times, and lower detection limit. However the potential of junctionless transistors as BioFETs still needs to be investigated. In the thesis we investigate different junctionless BioFET design strategies from simulation, analytical, and fabrication perspectives. This dissertation focuses on two types of junctionless BioFETs: the ion-sensitive and dielectric modulated. First we developed the surface potential based analytical models for ion-sensitive junctionless BioFET for pH sensing applications. We propose a new simulation equivalent model for electrolyte taking into account the site binding model (SBM) where the electrolyte is considered to be a stacked structure of stern layer, ion permeable layer and bulk electrolyte. Then a poly-Si based boron in-situ doped junctionless BioFET is fabricated by generic CMOS approach and tested for pH detection. Further for the detection of weakly charged biomolecules, the design considerations of junctionless embedded cavity dielectric modulated BioFET was investigated through surface potential based analytical and simulation model. Lastly, we report a novel biosensing scheme comprising two stages (the sensing and amplifying stages) based on the gate all around dielectric modulated junctionless BioFET.
... The advancement of semiconductor technology has led to mass production of low cost sensors with advantages such as real-time detection [2], high sensitivity at low concentrations [3], portable and convenient read out circuitry [4]. sensitive FETs for sensing of multiple analytes with considerable improvement in figure of merits (FOMs) for sensors such as sensitivity, linearity, limit of detection (LOD) and signal to noise ratio (SNR) [5], [6]. Majority of biomolecule sensing experiments take place in aqueous environments in polar solvents such as water. ...
... (a) Equivalent density of states (conduction band and valence band) for 3 molar concentrations of phosphate buffer solutions in(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) pH range (b) Experimental calibration of simulation model[24]. ...
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