2023/10/05 by Matthew J. Filipovich, Filipovich, Matthew J., Aleksei Malyshev +3
Computer Science · Engineering · #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Neural Networks and Reservoir Computing #Optical Network Technologies #Optics (physics.optics) #Photonic and Optical Devices #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.03679
openalex publication_date 2023/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.