Coupling Enhanced Diffractive Deep Neural Network with Structural Nonlinearity

作者：Ouling Wu, Chao Qian, Guangfeng You, Dashuang Liao, Nanxuan Wu, Hongsheng Chen · 发表于：Advanced Photonics Research · 年份：2025 · DOI：10.1002/adpr.202500038 · 被引用次数：6 · 研究领域：Neural Networks and Reservoir Computing、Photonic and Optical Devices、Random lasers and scattering media

The increasing complexity of deep learning models poses stringent requirements on electronic computers. Diffractive deep neural networks (D 2 NNs), as one of the most representative optical computing architectures, have emerged as a significant substitute for electronic‐based devices due to the advantages of high speed, low power consumption, and high parallelism. However, the absence of optical nonlinearity constrains the potential advancement of D 2 NNs. Recent progress in structural nonlinearity has offered a promising avenue for addressing this issue, but it necessitates complex digital data pre‐encoding. Herein, structural nonlinearity is introduced into D 2 NNs by incorporating encoding‐free data repetition layers, enabling high‐order optical nonlinearity while reducing the system complexity. The effectiveness of different data repetition manners demonstrates the robustness of this approach. Additionally, to enhance the design accuracy of D 2 NNs, a graph neural network framework is developed to characterize the coupling effects in metasurface layers and integrate it into D 2 NNs. This work provides a novel approach for the design of optical computing devices and holds significant importance for the development of high‐performance and highly integrated all‐optical devices.