Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization

Guardat en:

Dades bibliogràfiques
Publicat a:	Nanophotonics vol. 14, no. 23 (2025), p. 3761
Autor principal:	Chen, Yuheng
Altres autors:	Alexander Montes McNeil, Park, Taehyuk, Wilson, Blake A, Iyer, Vaishnavi, Bezick, Michael, Jae-Ik Choi, Ojha, Rohan, Mahendran, Pravin, Singh, Daksh Kumar, Chitturi, Geetika, Chen, Peigang, Do, Trang, Kildishev, Alexander V, Shalaev, Vladimir M, Moebius, Michael, Cai, Wenshan, Liu, Yongmin, Boltasseva, Alexandra
Publicat:	Walter de Gruyter GmbH
Matèries:	Machine learning Design optimization Data augmentation Quantum phenomena Control theory Data processing Multiscale analysis Measuring instruments Modelling Photonics Structural analysis Design parameters Numerical methods Optical properties
Accés en línia:	Citation/Abstract Full Text - PDF
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Descripció
Resum:	Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems.
ISSN:	2192-8606 2192-8614
DOI:	10.1515/nanoph-2025-0049
Font:	Advanced Technologies & Aerospace Database