All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing. (20th November 2020) - Hu, Lingxiang; Yang, Jing; Wang, Jingrui; Cheng, Peihong; Chua, Leon O.; Zhuge, Fei
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. Abstract : An all‐optically controlled (AOC) analog memristor is realized based on the relatively mature material InGaZnO. The memconductance is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. This device has promising applications in AOC spiking neural networks for highly efficient optoelectronic neuromorphic computing.