基于可重新配置的激发器件的光学神经形态计算
Zhihan Jin1, Hao Liu1, Tianhong Chen2
1College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Nano letters
|December 30, 2025
概括
研究人员使用范德瓦尔斯异构结构开发了一种新的光学神经形态计算方法. 这种方法通过控制光学激发动力学来实现实时图像处理和低功耗的人工智能.
科学领域:
- 光子学 是一个光子学.
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 光学神经形态计算是实时图像处理和低功耗AI的一个有前途的领域.
- 现有的方法在效率和可编程性方面面临挑战.
研究的目的:
- 引入和验证一种利用光学激发动态的新型计算范式.
- 用二维范德瓦尔斯异构结构来证明神经形态函数.
主要方法:
- 在2D材料中利用II型带对齐和高导电性,用于刺激控制.
- 采用电场调制,以在室温下达到高激子-子比率 (~7).
- 开发了准线性三元光发光,作为可调节的光学突触反应.
主要成果:
- 通过基板电压实现了突触重量的动态调整.
- 成功实现了用于图像消噪的卷积过.
- 经过充分连接的网络的模式识别,在噪音条件下达到98.7%的准确性.
结论:
- 建立了β-TeO2作为光学神经网络和自适应视觉系统的关键材料.
- 通过可编程光学响应重新定义了智能光子处理.
- 展示了先进,低功耗的人工智能应用的潜力.
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