Solid‐State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing

Abstract

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aelm.202101260.

All-Optically Controlled Artificial Synapses Based on Light-Induced Adsorption and Desorption for Neuromorphic Vision

“…Inspired by the neural structure and operation of biological visual systems where light stimuli are simultaneously sensed and processed by the retina, this technology thus saves the energy and reduces the latency time due to distributed and event-driven computation. Optoelectronic modulation devices, such as image sensors and photodetectors, have been combined with memory units to implement neuromorphic visual systems for energy-efficient image processing and object recognition. − In these systems, visual stimuli are usually perceived by optoelectronic devices (artificial photoreceptors) and computed by memory devices (artificial synapses). However, the physical separation between optoelectronic devices and memory devices causes high-energy consumption and a long latency time. ,, Therefore, it is imperative to develop an artificial optical synapse with the capability of integrating optical sensing and synaptic functions for neuromorphic visual systems.…”

Ultralow-power consumption photonic synapse transistors based on organic array films fabricated using a particular prepatterned-guided crystallizing strategy

“…The neuromorphic computing simulation in the ANN includes positive and negative weight values. 50 Accordingly, the conductance difference of two synaptic devices is defined as a synaptic weight 51 by W = G + + G − . When the change Δ W > 0, the relational W ↑ = G + ↑ + G − ↓ is used.…”

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

“…Moreover, they have since advanced their research to create versatile neuromorphic devices based on oxide ionic transistors. This field has attracted widespread attention as more and more researchers are interested in successfully implementing some essential ionic neural functions such as synaptic plasticity [67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85], synaptic filtering [86][87][88], synaptic learning rules [89][90][91][92][93][94], neuronal coding [95], neuronal integration [96], spatiotemporal information processing [32,97], reservoir computing [63], artificial neural networks [41,74,[98][99][100][101][102][103][104][105][106], and artificial sensory neurons [107][108]…”

“…The implementation of synaptic plasticity plays an essential role in realizing ionic dynamic neuromorphic computing. A wide variety of synaptic ionic computing behaviors such as post-synaptic currents [41,100,[116][117][118][119][120][121][122][123][124], short-term plasticity [29,64,69,[125][126][127][128][129][130][131][132][133][134][135][136][137], long-term plasticity [138][139][140][141], and synaptic learning rules [142][143][144] have been implemented by oxide ionic transistors.…”