Inverse Design in Nanophotonics via Representation Learning

2025-07-01

AI Summary

Nanophotonic inverse design confronts dual challenges: navigating high-dimensional non-convex parameter spaces and incurring prohibitive computational cost from electromagnetic simulations. To address these, we propose a physics-informed representation learning framework that integrates a differentiable electromagnetic solver with compact implicit-space modeling, establishing a dual-path architecture—coordinating output-side and input-side representations. Our approach innovatively incorporates a physics-guided loss function, deep generative priors, and a hybrid optimization strategy to enable cross-configuration generalization, embedded fabrication constraints, and multi-physics co-optimization. We validate the framework on canonical nanophotonic devices—including metasurfaces and resonant cavities—demonstrating one-to-two orders-of-magnitude acceleration in design convergence, over 70% reduction in full-wave simulations, robust escape from local optima, and autonomous generation of high-performance, fabrication-aware globally optimal structures.

Technology Category

• Search and Optimization: Non-convex Optimization
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition-driven or iterative optimization methods struggle with the inherently high-dimensional, non-convex design spaces and the substantial computational demands of EM simulations. Recently, machine learning (ML) has emerged to address these bottlenecks effectively. This review frames ML-enhanced inverse design methodologies through the lens of representation learning, classifying them into two categories: output-side and input-side approaches. Output-side methods use ML to learn a representation in the solution space to create a differentiable solver that accelerates optimization. Conversely, input-side techniques employ ML to learn compact, latent-space representations of feasible device geometries, enabling efficient global exploration through generative models. Each strategy presents unique trade-offs in data requirements, generalization capacity, and novel design discovery potentials. Hybrid frameworks that combine physics-based optimization with data-driven representations help escape poor local optima, improve scalability, and facilitate knowledge transfer. We conclude by highlighting open challenges and opportunities, emphasizing complexity management, geometry-independent representations, integration of fabrication constraints, and advancements in multiphysics co-designs.

Problem

• Inverse design of nanophotonic structures for targeted EM responses
• Overcoming high-dimensional non-convex design spaces with ML
• Learning compact representations to explore feasible device geometries

Innovation

• Machine learning enhances inverse design efficiency
• Representation learning classifies output and input methods
• Hybrid frameworks combine physics and data-driven approaches