Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Sep 20;22(19):7110. doi: 10.3390/s22197110.
A partitionable adaptive multilayer diffractive optical neural network is constructed to address setup issues in multilayer diffractive optical neural network systems and the difficulty of flexibly changing the number of layers and input data size. When the diffractive devices are partitioned properly, a multilayer diffractive optical neural network can be constructed quickly and flexibly without readjusting the optical path, and the number of optical devices, which increases linearly with the number of network layers, can be avoided while preventing the energy loss during propagation where the beam energy decays exponentially with the number of layers. This architecture can be extended to construct distinct optical neural networks for different diffraction devices in various spectral bands. The accuracy values of 89.1% and 81.0% are experimentally evaluated for MNIST database and MNIST fashion database and show that the classification performance of the proposed optical neural network reaches state-of-the-art levels.
构建了可分区自适应多层衍射光学神经网络，以解决多层衍射光学神经网络系统中的设置问题和灵活改变层数和输入数据大小的困难。当衍射器件被适当分区时，可以快速灵活地构建多层衍射光学神经网络，而无需重新调整光路，并且可以避免光学器件的数量随着网络层数线性增加，同时防止光束能量随着层数呈指数衰减而在传播过程中损失能量。该架构可扩展为在不同光谱带的不同衍射器件中构建不同的光学神经网络。在 MNIST 数据库和 MNIST 时尚数据库中，实验评估的准确率值分别为 89.1%和 81.0%，表明所提出的光学神经网络的分类性能达到了最新水平。
