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Organic Optoelectronic Synaptic Devices for Energy-Efficient Neuromorphic Computing.

Authors :
Li, Qingxuan
Wang, Tianyu
Hu, Xuemeng
Wu, Xiaohan
Zhu, Hao
Ji, Li
Sun, Qingqing
Zhang, David Wei
Chen, Lin
Source :
IEEE Electron Device Letters; Jul2022, Vol. 43 Issue 7, p1089-1092, 4p
Publication Year :
2022

Abstract

Organic materials with good biocompatibility and mechanical flexibility have great application potential in photonic neuromorphic computing. Here, the organic optoelectronic synapse for neuromorphic computing is fabricated on a flexible substrate. The excellent ferroelectricity of poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) endows the device with a memory window larger than 18 V and stable conductance modulation. The excellent photosensitive properties of 2,7-dioctyl[1] benzothieno[3,2-b][1]benzothiophene (C8-BTBT) enable the device to operate at an extremely low voltage of 0.25uV and achieve an ultralow energy consumption of 0.35fJ per event. In addition, under photoelectric dual modulation, the proposed synaptic devices can realize the simulation of biological synaptic behaviors, such as excitatory post-synaptic current (EPSC), long-term potentiation /depression (LTP/LTD). The neuromorphic computing function was verified using pattern recognition, with a recognition rate of up to 90.6% for handwritten digits. This research provides an effective way for the development of multifunctional artificial synaptic devices and artificial neural network systems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07413106
Volume :
43
Issue :
7
Database :
Complementary Index
Journal :
IEEE Electron Device Letters
Publication Type :
Academic Journal
Accession number :
157765527
Full Text :
https://doi.org/10.1109/LED.2022.3180346