Control of stochastic resonance response in a GaAs‐based nanowire field‐effect transistor

Abstract

Control of stochastic resonance (SR) response in a GaAs‐based nanowire FET is investigated for its application to nanoelectronics. The SR is a phenomenon in which the response to a weak signal is optimized by adding noise. Experiments clarify that the peak position is controlled by the gate offset voltage. The peak height also depends on the offset voltage and decreases when the peak position places in high noise region. Theoretical analysis indicates that the peak position depends only on the offset voltage whereas the height is also controlled by device dimensions. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Stochastic resonance in MoS2 photodetector

“…SR has now been observed in a wide variety of physical systems including bi-stable ring lasers 9 , optical heterodyning 18 , electronic paramagnetic resonance 19 , superconducting quantum interference devices (SQUID) 7 , and tunnel diodes 8 , among others. While there are some reports of experimental demonstration of SR in field-effect transistors (FETs) based on carbon nanotubes [20][21][22] , GaAs nanowires [23][24][25][26] , and organic semiconductors 27 , the strength of SR is yet to be exploited in solid-state sensors.…”

Detection of weak biological signal utilizing stochastic resonance in a GaAs-based nanowire FET and its parallel summing network

“…3(b), since the voltage difference between the signal and the threshold voltage in this system was smaller than that in the previous case. 44) The SNR from the FET network was approximately 10 dB higher than that of the unipolar lead system for any number of units. This result demonstrated the feasibility of the use of SR in the nanowire FETs for detecting the EMG contaminated with intrinsic noise in the human body.…”

Bi2O2Se-Based Bimode Noise Generator for the Application of Generative Adversarial Networks

“…To alleviate those disturbing effects, many efforts in the electronic field endeavor to diminish those “detrimental” noises; for example, introducing a double fullerene layer for trap passivation to suppress the shot noise and flick noise in a perovskite photodetector; the shunting electrode, working as a blanking unit, exhibited a record low dark current in a perovskite X-ray detector; the memristor in the threshold switching mode could be utilized as a selector, which largely suppressed the sneak path current in a memristive crossbar array; and applying small-voltage pulses could denoise the random telegraph noising in a memristive crossbar array to precisely control the conductance levels . Interestingly, those “detrimental” noise effects in electronic and photonic devices turn out to be constructive in many emerging fields: stochastic resonance in carbon nanotubes, GaAs nanowires, and MoS 2 photodetectors for detecting low-amplitude signals; − entropy sources for random number generation, including tungsten diselenide (WSe 2 ) field-effect transistors or hexagonal boron nitride (h-BN) and Bi 2 O 2 Se-based memristors (BMs); − as well as some emerging reported pioneering works that implemented the memristive intrinsic noise for accelerating the neuromorphic computing, such as realizing the stochastic gradient descent, improving the evolution, and inducing a diversity of solutions for a Hopfield neural network, suppressing the overfitting and realization of generative adversarial networks (GANs). − Compared to fabricating artificial synapses, implementing noises in neural networks, such as GANs, could be another feasible approach for neuromorphic computing and require further research effort.…”