Role of Spatial Coherence in Diffractive Optical Neural Networks

TL;DR

This paper investigates how spatial coherence affects diffractive optical neural networks (DONNs), proposing a simulation method for incoherent inputs and evaluating performance on digit recognition tasks.

Contribution

It introduces a numerical approach to simulate DONNs with incoherent and partially coherent light, addressing a gap in understanding their real-world applicability.

Findings

DONNs performance varies with input coherence levels

Proposed simulation method efficiently models incoherent illumination

Demonstrated on MNIST dataset with varying coherence

Abstract

Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations of DONNs have only been performed using coherent light. However, many real-world DONN applications require consideration of the spatial coherence properties of the optical signals. Here, we study the role of spatial coherence in DONN operation and performance. We propose a numerical approach to efficiently simulate DONNs under incoherent and partially coherent input illumination and discuss the corresponding computational complexity. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits to process light with varying spatial coherence.