Organic Optoelectronic Synaptic Devices for Energy-Efficient Neuromorphic Computing

作者：Qingxuan Li, Tianyu Wang, Xuemeng Hu, Xiaohan Wu, Hao Zhu, Ji Li, Qingqing Sun, David Wei Zhang, Lin Chen · 发表于：IEEE Electron Device Letters · 年份：2022 · DOI：10.1109/led.2022.3180346 · 被引用次数：41 · 研究领域：Advanced Memory and Neural Computing、Photoreceptor and optogenetics research、Neural Networks and Reservoir Computing

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.