Optimizing optical neural network design for enhanced compatibility with analog computation.

This paper breaks away from traditional approaches that merely emulate digital neural networks. Using Mach-Zehnder interferometer (MZI) networks as a case study, we explore the impact of the inherent properties of analog computation on performance and identify the characteristics that optical neural networks (ONNs) components should possess to better adapt to these specific properties. Specifically, we examine the influence of analog computation on bias power and activation functions, as well as the impact of optical pruning on ONN's performance. The results show that a suitably larger bias power relative to normalized data and concave activation functions are more compatible with the characteristics of ONNs. These factors can significantly improve classification accuracy across different datasets and values, with improvements reaching up to 35%. Additionally, optical pruning reduces the number of MZIs by two-thirds while maintaining performance. Moreover, these measures significantly enhance the robustness of ONNs against MZI losses and phase errors. Although this research primarily focuses on feedforward MZI-based networks, the proposed design principles are widely applicable to other types of ONNs.