用于联合光学分类的2D光学晶体集成的多波长衍射光学神经网络
Yuanyuan Zhang1, Kuo Zhang1,2, Pei Hu3
1School of Science, Minzu University of China, Beijing 100081, China.
Nanophotonics (Berlin, Germany)
|September 2, 2025
概括
这项研究引入了一种新的多波长衍射光学神经网络 (DONN), 基于光子晶体的架构在视觉分类任务中实现了高精度,为先进的光子处理器铺平了道路.
科学领域:
- 光子学
- 机器学习
- 光学计算
背景情况:
- 光学神经网络 (ONN) 由于具有高平行性,带宽和低功耗,因此比电子计算具有优势.
- 在芯片上的衍射光学神经网络 (DONN) 是集成的,节能机器学习的关键.
- 目前的DONN受限于单波长操作,限制了计算并行性.
研究的目的:
- 提出并展示多波长视觉分类架构,PhC-DONN,用于增强计算吞吐量.
- 在衍射计算中利用波长作为多维复合的自由度.
- 建立多波长光学神经网络的新型光学分类范式.
主要方法:
- 两维光子晶体 (PhC) 组件与衍射计算单元的集成.
- 用于多波长特征提取的PhC卷积层的开发.
- 实现平行光场调制的三级衍射层和波长非线性计算的PhC非线性激活层.
主要成果:
- PhC-DONN的分类准确度很高:在MNIST上达到99. 09%,在CIFAR-10上达到66. 41%,在KTH上达到92. 25%.
- 与传统的DONN相比,该架构的计算吞吐量提高了32倍.
- 通过波长平行分类在单个光传播通道中实现多通道推断.
结论:
- 拟议的PhC-DONN成功实现了多波长分类机制,显著提高了计算吞吐量和准确性.
- 这项工作为构建大规模光子智能并行处理器提供了可行的途径.
- 这种新型架构推进了多波长光学神经网络的光学分类范式.
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