记忆电阻器
冯·诺依曼建筑
瓶颈
神经形态工程学
计算机科学
非常规计算
内存处理
计算
计算机体系结构
材料科学
电子工程
分布式计算
人工神经网络
人工智能
嵌入式系统
算法
工程类
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作者
Jing Yang,Lingxiang Hu,Liufeng Shen,Jingrui Wang,Peihong Cheng,Huanming Lu,Fei Zhuge,Zhizhen Ye
出处
期刊:Cornell University - arXiv
日期:2021-01-01
标识
DOI:10.48550/arxiv.2108.02739
摘要
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 the operation of our AOC memristor which demonstrates superior operation stability. Based on this and its structural simplicity, the device has attractive application prospects for the next generation of computing systems.
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