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《工程(英文)》 >> 2021年 第7卷 第10期 doi: 10.1016/j.eng.2020.07.032

可见光波段的深度衍射神经网络

a Center of Ultra-precision Optoelectronic Instrument, Harbin Institute of Technology, Harbin 150001, China.
b Nanofabrication Facility, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China.
c Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Ministry of Education, Harbin Institute of Technology, Harbin
150001, China

收稿日期: 2020-02-10 修回日期: 2020-05-19 录用日期: 2020-07-20 发布日期: 2021-02-13

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摘要

基于衍射光学元件的光学深度学习在并行处理、计算速度和计算效率方面有着独特优势。深度衍射神经网络(D2NN)是其中一项具有里程碑意义的研究工作。D2NN在太赫兹波段通过3D打印进行神经网络的物理固化。鉴于太赫兹波段下存在的粒子间耦合限制和材料损耗,本文将D2NN的应用波段延展至可见光波段,并提出了包括修订公式在内的一般理论,解决了工作波长、人工神经元特征尺寸和加工制备之间的矛盾。在632.8 nm的工作波长下,本文提出了一种新颖的可见光D2NN分类器,可用于原始目标(手写数字0~9)和已更改目标(被遮盖和涂改目标)的目标识别。本文获得的实验分类精度(84%)和数值分类精度(91.57%)量化了理论设计和制造系统性能之间的匹配程度。本文所提出的一般理论模型可将D2NN应用于各种实际问题或设计全新的应用场景。

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