Machine-learning-enabled characterization of individual ring resonators in integrated photonic lattices

2026/01/14 by Elizabeth Louis Pereira, A. Hashemi, Amin Hashemi +6 · 1 voice
Computer Science · Engineering · Physics and Astronomy · #Advanced Fiber Laser Technologies #Neural Networks and Reservoir Computing #Photonic and Optical Devices #cond-mat.dis-nn #physics.optics

paper · pdf · doi:10.1063/5.0324151

arxiv published 2026/01/14 · arxiv updated 2026/01/14 · openalex publication_date 2026/07/01 · openalex created_date 2026/08/01 · openalex updated_date 2026/08/03

Abstract

Accurately determining the underlying physical parameters of individual elements in integrated photonics is increasingly difficult as device architectures become more complex. Inferring these parameters directly from spectral measurements of the system as a whole provides a practical alternative to traditional calibration, allowing characterization of photonic systems without relying on detailed device-specific models. Here, we introduce a supervised machine-learning strategy to learn the onsite losses and resonant frequency shifts of each individual ring in an array of coupled ring resonators from measured spectral power distributions of the whole array. The neural network infers these parameters with high accuracy across multiple experimental configurations. Our methodology provides a scalable and non-invasive method for extracting intrinsic parameters in coupled photonic platforms, paving the way for future development of automated calibration and control methods.

Citations

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Discussions

• Today in arXiv (arxiv.org/abs/2601.09423), machine-learning enabled characterization of individual ring resonators in integrated photonic lattices, together with Elizabeth Louis Pereira, Amin Hashemi, [bsky, 2 points, 0 comments]