Optical neural architectures (ONAs) use coding elements with optimized
physical parameters to perform intelligent measurements. However, fabricating
ONAs while maintaining design performances is challenging. Limitations in
fabrication techniques often limit the realizable precision of the trained
parameters. Physical constraints may also limit the range of values the
physical parameters can hold. Thus, ONAs should be trained within the
implementable constraints. However, such physics-based constraints reduce the
training objective to a constrained optimization problem, making it harder to
optimize with existing gradient-based methods. To alleviate these critical
issues that degrade performance from simulation to realization we propose a
physics-informed quantization-aware training framework. Our approach accounts
for the physical constraints during the training process, leading to robust
designs. We evaluate our approach on an ONA proposed in the literature, named a
diffractive deep neural network (D2NN), for all-optical phase imaging and for
classification of phase objects. With extensive experiments on different
quantization levels and datasets, we show that our approach leads to ONA
designs that are robust to quantization noise.

光学神经架构（ONA）使用具有优化的物理参数的编码元素进行智能测量，提出了一种物理信息量化感知训练框架，该方法在训练过程中考虑了物理约束，导致了稳健的设计。在不同量化水平和数据集上进行了大量实验，表明我们的方法导致了对量化噪声稳健的 ONA 设计。