Impact of common fabrication errors on the performance of diffractive neural networks

We numerically investigate the performance of optical implementations of deep neural network for complex field data processing in the form of multi-layer nanoscale diffractive neural networks, trained to perform image classification tasks. We discuss the parameter optimization and the limitations that fabrication errors put on the performance of such direct phase retrieval systems. The diffractive neural networks studied here may have transformative impact on adaptive optics, data processing and sensing and may be crucial in the development of robust and generalized quantitative phase imaging methods.