Physically interpretable diffractive optical networks for high-dimensional vortex mode sorting

TL;DR

This paper introduces physically interpretable diffractive optical networks for high-dimensional vortex mode sorting, revealing physical transformation rules and phenomena that enhance understanding and design of optical systems.

Contribution

It provides a novel physical interpretation framework for diffractive networks, linking physical transformations to network performance and enabling efficient design for mode sorting tasks.

Findings

Physical transformation division phenomenon observed with increasing masks.

Physical interpretation aids in designing high-performance parameter-varying networks.

Revealed physical transformation rules at the layer level of trained networks.

Abstract

Despite the significant progress achieved by diffractive optical networks in diverse computing tasks, such as mode multiplexing and demultiplexing, investigations into the physical meanings behind complex diffractive networks at the layer level have been quite limited. Here, for highdimensional vortex mode sorting tasks, we show how various physical transformation rules for each layer within trained diffractive networks can be revealed under properly defined input/output mode relations. An intriguing physical transformation division phenomenon, associated with the saturated sorting performance of the system, has been observed with an increasing number of masks. In addition, we have also demonstrated the use of physical interpretation for efficiently designing parameter-varying networks with high performance. These physically interpretable optical networks resolve the contradiction between rigorous physical theorems and operationally vague network structures, paving the way for designing and understanding systems for various mode conversion tasks, and inspiring further interpretation of diffractive networks in advanced tasks and other network structures.