Electrolyte-gated transistors for neuromorphic applications

Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck.To enhance computing performance,neuromorphic computing systems that can simulate the function of the human brain are being developed.Artificial synapses are essential electronic devices for neuromorphic architectures,which have the ability to perform signal processing and storage between neighboring artificial neurons.In recent years,electrolyte-gated transistors(EGTs)have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications.Among the various electronic devices,EGT-based artificial synapses offer the benefits of good stability,ultra-high linearity and repeated cyclic symmetry,and can be constructed from a variety of materials.They also spatially separate“read”and“write”operations.In this article,we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications.We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses.Then,we review different types of channels and electrolyte materials for EGT-based artificial synapses.Finally,we review the potential applications in biological functions.

Aqueous-processable,naphthalene diimide-based polymers for eco-friendly fabrication of high-performance,n-type organic electrolyte-gated transistors

Organic electrolyte-gated transistors(OEGTs) have the benefits of low power consumption and large current modulation.Nevertheless,the electrical performance of n-type OEGTs lags far behind that of p-type OEGTs.In this study,we design a series of polymers,P(NDITEG-T) and P(NDIMTEG-T),comprising a naphthalene diimide backbone for n-type charge transport and oligo(ethylene glycol)(OEG) side chains for high ionic conductivity and eco-friendly solution processing.The incorporation of the OEG chain facilitates the electrochemical doping of the semiconductor by ions to realize high-performance,n-type OEGTs.Notably,in OEGTs,P(NDITEG-T) achieves a high electron mobility of 1.0 × 10^(-1) cm^(2) V^(-1) s^(-1),which represents the highest value reported for solution-processed,n-type OEGTs.It is noted that the fabrication of the OEGTs is achieved by solution processing with eco-friendly ethanol/water mixtures in virtue of the hydrophilic OEG chains.This work demonstrates the molecular design of the P(NDITEG-T) polymer and its significant ability to produce aqueous-processable,high-performance,and n-type OEGTs.

A multi-terminal ion-controlled transistor with multifunctionality and wide temporal dynamics for reservoir computing

Reservoir computing(RC)is an energy-efficient computational framework with low training cost and high efficiency in processing spatiotemporal information.The state-of-the-art fully memristor-based hardware RC system suffers from bottlenecks in the computation efficiencies and accuracy due to the limited temporal tunability in the volatile memristor for the reservoir layer and the nonlinearity in the nonvolatile memristor for the readout layer.Additionally,integrating different types of memristors brings fabrication and integration complexities.To overcome the challenges,a multifunctional multi-terminal electrolyte-gated transistor(MTEGT)that combines both electrostatic and electrochemical doping mechanisms is proposed in this work,integrating both widely tunable volatile dynamics with high temporal tunable range of 10^(2) and nonvolatile memory properties with high long-term potentiation/long-term depression(LTP/LTD)linearity into a single device.An ion-controlled physical RC system fully implemented with only one type of MTEGT is constructed for image recognition using the volatile dynamics for the reservoir and nonvolatility for the readout layer.Moreover,an ultralow normalized mean square error of 0.002 is achieved in a time series prediction task.It is believed that the MTEGT would underlie next-generation neuromorphic computing systems with low hardware costs and high computational performance.