Cooperative optimization of neural-network-based diffraction simulations and benchtop spatial light modulators

Marshall B. Lindsay; Scott D. Kovaleski; Andy G. Varner; Charlie Veal; Derek T. Anderson; Stanton R. Price; Steven R. Price

doi:10.1117/12.3013482

7 June 2024 Cooperative optimization of neural-network-based diffraction simulations and benchtop spatial light modulators

Marshall B. Lindsay, Scott D. Kovaleski, Andy G. Varner, Charlie Veal, Derek T. Anderson, Stanton R. Price, Steven R. Price

Author Affiliations +

Proceedings Volume 13042, Advanced Optics for Imaging Applications: UV through LWIR IX; 1304207 (2024) https://doi.org/10.1117/12.3013482
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The integration of neural networks and differentiable scalar wave optics has facilitated a modern approach to the design of optical systems, where simulation and optimization are carried out concurrently. These techniques encode the equations of wavefront propagation and modulation directly as layers of a neural network where the forward pass carries out simulation and the backward pass carries out optimization using the backpropagation algorithm. While this allows standard optical optimization as well as classifier-driven optimization of diffractive optics, it suffers from the ubiquitous simulation-to-reality gap. Identifying, characterizing, and ultimately reducing this simulation-to-reality gap is an ever-present objective – as the adage goes, “all models are wrong, some are useful.” To this end, this work extends recent advancements in physics-aware training where an optimizable physical device is used alongside in-silico simulation. By comparing the simulation output with the measured result from the physical device, an additional error term is introduced to the optimization objective. This work analyzes the multi-criteria loss function by varying weighting terms and analyzing performance. It is found that minimizing this new error term reduces the simulation-to-reality gap but at the cost of device performance. The optimizable device in this work is implemented using a reprogrammable spatial light modulator.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Marshall B. Lindsay, Scott D. Kovaleski, Andy G. Varner, Charlie Veal, Derek T. Anderson, Stanton R. Price, and Steven R. Price "Cooperative optimization of neural-network-based diffraction simulations and benchtop spatial light modulators", Proc. SPIE 13042, Advanced Optics for Imaging Applications: UV through LWIR IX, 1304207 (7 June 2024); https://doi.org/10.1117/12.3013482

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