神经形态工程学
记忆电阻器
材料科学
光电子学
半导体
数码产品
计算机科学
纳米技术
光子学
人工神经网络
电子工程
电气工程
人工智能
工程类
作者
Lili Hu,Jing Yang,Jian Wang,Peihong Cheng,Leon O. Chua,Fei Zhuge
标识
DOI:10.1002/adfm.202005582
摘要
Abstract Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh‐density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all‐optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light‐induced electron trapping and detrapping. It is found that the light‐induced multiple memconductance states are nonvolatile. Furthermore, spike‐timing‐dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.
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