  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.
