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arXiv · 2310.03679

Role of Spatial Coherence in Diffractive Optical Neural Networks

Matthew J. Filipovich·Aleksei Malyshev·A. I. Lvovsky
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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.

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Matthew J. Filipovich, Aleksei Malyshev, A. I. Lvovsky. 2023-10-05. Role of Spatial Coherence in Diffractive Optical Neural Networks. https://arxiv.org/abs/2310.03679

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