Energy‐efficient organic photoelectric synaptic transistors with environment‐friendly CuInSe 2 quantum dots for broadband neuromorphic computing

作者：Junyao Zhang, Ziyi Guo, Tongrui Sun, Pu Guo, Xu Liu, Huaiyu Gao, Shilei Dai, Lize Xiong, Jia Huang · 发表于：SmartMat · 年份：2023 · DOI：10.1002/smm2.1246 · 被引用次数：61 · 研究领域：Advanced Memory and Neural Computing、Photoreceptor and optogenetics research、Neural Networks and Reservoir Computing

Abstract Photoelectric synaptic device is a promising candidate component in brain‐inspired high‐efficiency neuromorphic computing systems. Implementing neuromorphic computing with broad bandwidth is, however, challenging owing to the difficulty in realizing broadband characteristics with available photoelectric synaptic devices. Herein, taking advantage of the type‐II heterostructure formed between environmentally friendly CuInSe 2 quantum dots and organic semiconductor, broadband photoelectric synaptic transistors (BPSTs) that can convert light signals ranging from ultraviolet (UV) to near‐infrared (NIR) into post‐synaptic currents are demonstrated. Essential synaptic functions, such as pair‐pulse facilitation, the modulation of memory level, long‐term potentiation/depression transition, dynamic filtering, and learning‐experience behavior, are well emulated. More significantly, benefitting from broadband responses, information processing functions, including arithmetic computing and pattern recognition can also be simulated in a broadband spectral range from UV to NIR. Furthermore, the BPSTs exhibit obvious synaptic responses even at an ultralow operating voltage of −0.1 mV with an ultralow energy consumption of 75 aJ per event, and show their potential in flexible electronics. This study presents a pathway toward the future construction of brain‐inspired neural networks for high‐bandwidth neuromorphic computing utilizing energy‐efficient broadband photoelectric devices.