All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing,Advanced Functional Materials

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All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing
Advanced Functional Materials ( IF 18.5 ) Pub Date : 2020-11-20 , DOI: 10.1002/adfm.202005582
Lingxiang Hu _{1,

2} , Jing Yang ₁ , Jingrui Wang ₁ , Peihong Cheng ₁ , Leon O. Chua ₃ , Fei Zhuge _{1,

2,

4}

Affiliation

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.

中文翻译：

光电神经形态计算的全光控忆阻器

神经形态计算（NC）是新一代的人工智能。忆阻器由于其超高密度3D集成的可行性和超低能耗而成为有前途的NC候选者。与传统的电气忆阻器相比，新兴的光电忆阻器具有结合光子学和电子学优点的能力，因此更具吸引力。但是，无法用光可逆地调节薄膜，严重限制了光电数控的发展。此处，实现了全光控（AOC）模拟忆阻器，其忆阻通过仅改变控制光的波长即可在连续范围内可逆调节。该器件基于相对成熟的半导体材料InGaZnO和光诱导电子的俘获和去俘获的半导调谐机制。发现光诱导的多个介导状态是非易失性的。此外，可以在此AOC忆阻器中模拟依赖于尖峰时序的可塑性学习，表明其在AOC尖峰神经网络中用于高效光电NC的潜在应用。

更新日期：2021-01-22

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