Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization

2025-06-24

AI Summary

Photonic device development faces significant challenges, including high computational overhead, expensive experimentation, complex design spaces, characterization uncertainty, and poor fabrication robustness. To address these, this work proposes an end-to-end machine learning–enabled co-design framework that integrates multi-scale modeling, generative design optimization, surrogate-accelerated electromagnetic simulation, GAN-based data augmentation, active learning–guided experimental characterization, and reinforcement learning–driven adaptive fabrication control. The framework enables efficient design exploration and closed-loop process optimization under noise and uncertainty. Experimental validation demonstrates substantial reductions in both simulation and experimental costs (average >60% decrease), accelerated design convergence (≈50% fewer iterations), improved fabrication yield, and enhanced generalizability across diverse photonic fabrication platforms. This scalable methodology supports rapid iterative deployment of complex photonic systems for applications in communications, sensing, and quantum technologies.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Overcoming computational and financial costs in photonic device development
• Addressing large optimization landscapes and characterization uncertainties
• Improving fabrication processes with machine learning techniques

Innovation

• Machine learning accelerates photonic device optimization
• Generative models enhance noisy measurement modeling
• Reinforcement learning improves fabrication processes