Characterization of scattering systems using multi-plane neural networks

Suraj Goel; Claudio Conti; Saroch Leedumrongwatthanakun; Mehul Malik

doi:10.1117/12.3016985

17 June 2024 Characterization of scattering systems using multi-plane neural networks

Suraj Goel, Claudio Conti, Saroch Leedumrongwatthanakun, Mehul Malik

Proceedings Volume 13023, Computational Optics 2024; 1302305 (2024) https://doi.org/10.1117/12.3016985
Event: SPIE Optical Systems Design, 2024, Strasbourg, France

Abstract

In this work, we present a method for characterizing the transmission matrices of complex scattering media using a physics-informed, multi-plane neural network (MPNN) without the requirement of a known optical reference field. In contrast to previous techniques, our method is able to measure complete information about the transmission matrix, which is necessary for coherent control of light through a complex medium. Here, we design a neural network that describes the exact physical apparatus consisting of a trainable layer describing the unknown transmission matrix. We then employ randomized measurements to train the neural network which accurately recovers the transmission matrix of a commercial multi-mode fiber. We demonstrate how our method is significantly more accurate, and noise-robust than the standard method of phase-stepping holography and show how it can be generalized to characterize a cascade of transmission matrices. This work presents an essential tool for accurate light control through complex media, with applications ranging from classical optical networks, biomedical imaging, to quantum information processing.

Conference Presentation

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

Citation Download Citation

Suraj Goel, Claudio Conti, Saroch Leedumrongwatthanakun, and Mehul Malik "Characterization of scattering systems using multi-plane neural networks", Proc. SPIE 13023, Computational Optics 2024, 1302305 (17 June 2024); https://doi.org/10.1117/12.3016985

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