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Gate Modulation of Excitatory and Inhibitory Synaptic Plasticity in a Low-Temperature Polysilicon Thin Film Synaptic Transistor
Neuromorphic computing with intelligent power-efficient data processing has become an innovative technology to overcome the performance bottleneck of traditional von Neumann-type computing architecture. As an essential element to construct a neuromorphic system, a kind of artificial synapse with hig...
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Published in: | ACS applied electronic materials 2019-01, Vol.1 (1), p.132-140 |
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Main Authors: | , , , , , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Neuromorphic computing with intelligent power-efficient data processing has become an innovative technology to overcome the performance bottleneck of traditional von Neumann-type computing architecture. As an essential element to construct a neuromorphic system, a kind of artificial synapse with high technology maturity, rich functionality, and homeostatic regulation based on simple and robust mechanism is in urgent demand. Here, we propose the dual-gate low-temperature polycrystalline silicon thin film transistor to be a prospective candidate for scalable biomimetic synapse. Fundamental bilingual homosynaptic behaviors, including excitatory postsynaptic current, inhibitory postsynaptic current, and paired pulse facilitation, have been successfully emulated based on the charge trapping mechanism under electric pulse stimulation at either top or bottom gates. The strength of the top-gated induced excitatory and inhibitory responses can be dynamically modulated by the electrical biases at the modulatory bottom gate, indicating the realization of heterosynaptic plasticity. Furthermore, the transition between excitatory and inhibitory modes can be easily controlled by the interplay of the voltage biases at top and bottom gates. These results indicate the commercial thin-film transistor technology could find its novel fundamental role in the emerging artificial intelligence era. |
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ISSN: | 2637-6113 2637-6113 |
DOI: | 10.1021/acsaelm.8b00060 |