Organic Optoelectronic Synaptic Devices for Energy-Efficient Neuromorphic Computing

被引:21
|
作者
Li, Qingxuan [1 ,2 ]
Wang, Tianyu [1 ,2 ]
Hu, Xuemeng [1 ,2 ]
Wu, Xiaohan [1 ,2 ]
Zhu, Hao [1 ,2 ]
Ji, Li [1 ,2 ]
Sun, Qingqing [1 ,2 ]
Zhang, David Wei [1 ,2 ]
Chen, Lin [1 ,2 ]
机构
[1] Fudan Univ, Sch Microelect, State Key Lab ASIC & Syst, Shanghai 200433, Peoples R China
[2] Zhangjiang Fudan Int Innovat Ctr, Shanghai 201203, Peoples R China
基金
中国博士后科学基金;
关键词
Synapses; Neuromorphic engineering; Biology; Pattern recognition; Transistors; Training; Photonics; Organic artificial synapse; photoelectric dual modulation; short-term and long-term plasticity; neuromorphic computing; ARTIFICIAL SYNAPSE; SENSOR;
D O I
10.1109/LED.2022.3180346
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:1089 / 1092
页数:4
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