MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure

2025-03-02

AI Summary

Photonic device inverse design faces bottlenecks including slow electromagnetic simulation, poor robustness, low manufacturability, absence of standardized evaluation benchmarks, and limited interpretability. To address these challenges, we introduce MAPS—the first open-source, multi-fidelity AI-enhanced infrastructure tailored for photonic integrated circuit design. MAPS comprises three core components: MAPS-Data (physics-guided, multi-source synthetic data generation), MAPS-Train (an AI training framework integrating physics-informed constraints and multi-fidelity modeling), and MAPS-InvDes (an interpretable inverse design toolchain). By synergistically coupling adjoint-based optimization, process variation modeling, and physics-driven loss functions, MAPS accelerates simulation by several orders of magnitude. Concurrently, we release a standardized benchmark suite and a high-quality, expert-annotated dataset. This work significantly improves design efficiency, reproducibility, and cross-disciplinary collaboration between academia and industry, advancing the practical deployment of AI for photonics.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Inverse design has emerged as a transformative approach for photonic device optimization, enabling the exploration of high-dimensional, non-intuitive design spaces to create ultra-compact devices and advance photonic integrated circuits (PICs) in computing and interconnects. However, practical challenges, such as suboptimal device performance, limited manufacturability, high sensitivity to variations, computational inefficiency, and lack of interpretability, have hindered its adoption in commercial hardware. Recent advancements in AI-assisted photonic simulation and design offer transformative potential, accelerating simulations and design generation by orders of magnitude over traditional numerical methods. Despite these breakthroughs, the lack of an open-source, standardized infrastructure and evaluation benchmark limits accessibility and cross-disciplinary collaboration. To address this, we introduce MAPS, a multi-fidelity AI-augmented photonic simulation and inverse design infrastructure designed to bridge this gap. MAPS features three synergistic components: (1) MAPS-Data: A dataset acquisition framework for generating multi-fidelity, richly labeled devices, providing high-quality data for AI-for-optics research. (2) MAPS-Train: A flexible AI-for-photonics training framework offering a hierarchical data loading pipeline, customizable model construction, support for data- and physics-driven losses, and comprehensive evaluations. (3) MAPS-InvDes: An advanced adjoint inverse design toolkit that abstracts complex physics but exposes flexible optimization steps, integrates pre-trained AI models, and incorporates fabrication variation models. This infrastructure MAPS provides a unified, open-source platform for developing, benchmarking, and advancing AI-assisted photonic design workflows, accelerating innovation in photonic hardware optimization and scientific machine learning.

Problem

• Addresses challenges in photonic device optimization and manufacturability.
• Lacks open-source infrastructure for AI-assisted photonic simulation and design.
• Provides a unified platform for AI-augmented photonic design workflows.

Innovation

• Multi-fidelity AI-augmented photonic simulation infrastructure
• Open-source standardized design and evaluation platform
• Adjoint inverse design toolkit with AI integration