In-memory computing based on all-optically controlled memristor
Jing Yang, Lingxiang Hu, Liufeng Shen, Jingrui Wang, Peihong Cheng,, Huanming Lu, Fei Zhuge, Zhizhen Ye

TL;DR
This paper introduces an all-optically controlled memristor that uses light to reversibly tune conductance without microstructure change, enabling stable in-memory computing for AI applications.
Contribution
It presents a novel light-controlled memristor with a simple structure that avoids microstructure changes, enhancing stability for in-memory computing.
Findings
Reversibly tunes conductance with light irradiation.
No microstructure change during operation.
Suitable for neuromorphic and logic computing.
Abstract
Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for real-time processing of rapidly growing data. Memristive in-memory computing is a promising candidate for highly efficient data processing. However, performance of memristors varies significantly because of microstructure change induced by electric-driven matter migration. Here, we propose an all-optically controlled (AOC) memristor with a simple Au/ZnO/Pt sandwich structure based on a purely electronic tuning mechanism of memconductance. The memconductance can be reversibly tuned only by light irradiation with different wavelengths. The device can be used to perform in-memory computation such as nonvolatile neuromorphic computing and Boolean logic functions. Moreover, no microstructure change is involved during…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Photoreceptor and optogenetics research · Neural Networks and Reservoir Computing
