Recent Advances in Transistor‐Based Artificial Synapses

By Shilei Dai, Yiwei Zhao, Yan Wang, Junyao Zhang, Lu Fang, Shu Jin, Yinlin Shao, Jia Huang. Advanced Functional Materials, 2019

546 citations recorded by the source corpus.

Simulating biological synapses with electronic devices is a re‐emerging field of research. It is widely recognized as the first step in hardware building brain‐like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor‐based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor‐based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor‐based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor‐based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor‐based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor‐based artificial synapses are also presented.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a6001696ae7bb67e235b838f7d40658d7bb364fc

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1. Bin Feng
2. Yu Xiao
3. Wenxian Wang
4. Chengjie Du
5. Tianming Sun
6. Jin Peng
7. Lei Liu
8. Zehua Li

Citations in the corpus

• Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses (2023, background): “…input signal from the presynaptic neuron is transmitted to the drain of the postsynaptic neuron by regulating the electric field intensity of the gate and controlling the magnitude of the source–drain current, thus mimicking signal transmission between the presynaptic and postsynaptic neurons [83].”