As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption. However, there is a mismatch, i.e., significant prediction accuracy loss, between the DONN numerical modelling and physical optical device deployment, because of the interpixel interaction within the diffractive layers. In this work, we propose a physics-aware diffractive optical neural network training framework to reduce the performance difference between numerical modeling and practical deployment. Specifically, we propose the roughness modeling regularization in the training process and integrate the physics-aware sparsification method to introduce sparsity to the phase masks to reduce sharp phase changes between adjacent pixels in diffractive layers. We further develop $2\pi$ periodic optimization to reduce the roughness of the phase masks to preserve the performance of DONN. Experiment results demonstrate that, compared to state-of-the-arts, our physics-aware optimization can provide $35.7\%$, $34.2\%$, $28.1\%$, and $27.3\%$ reduction in roughness with only accuracy loss on MNIST, FMNIST, KMNIST, and EMNIST, respectively.

翻译：作为超越CMOS的下一代器件/电路技术代表，衍射光学神经网络（DONN）凭借极快计算速度（光速）和低能耗展现出优于传统深度神经网络的显著优势。然而，由于衍射层内像素间相互作用，DONN数值建模与物理光学器件部署之间存在失配，即显著的预测精度损失。本文提出一种物理感知的衍射光学神经网络训练框架，以缩小数值建模与实际部署之间的性能差异。具体而言，我们在训练过程中引入粗糙度建模正则化，并集成物理感知稀疏化方法为相位掩膜引入稀疏性，从而减少衍射层中相邻像素间的剧烈相位变化。进一步开发了$2\pi$周期优化以降低相位掩膜的粗糙度，从而保持DONN的性能。实验结果表明，与现有最优方法相比，我们的物理感知优化在MNIST、FMNIST、KMNIST和EMNIST数据集上分别实现了35.7%、34.2%、28.1%和27.3%的粗糙度降低，且仅伴随精度损失。