Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, circuit design, system architecture, and AI algorithms. The absence of a mature electronic-photonic design automation (EPDA) toolchain leads to long, inefficient design cycles and limits cross-disciplinary innovation and co-evolution. In this work, we present a cross-layer co-design and automation framework aimed at democratizing photonic AI system development. We begin by introducing our architecture designs for scalable photonic edge AI and Transformer inference, followed by SimPhony, an open-source modeling tool for rapid EPIC AI system evaluation and design-space exploration. We then highlight advances in AI-enabled photonic design automation, including physical AI-based Maxwell solvers, a fabrication-aware inverse design framework, and a scalable inverse training algorithm for meta-optical neural networks, enabling a scalable EPDA stack for next-generation electronic-photonic AI systems.

翻译：光子学正逐渐成为高性能人工智能系统和科学计算的基石技术，其具备无与伦比的速度、并行性和能效。尽管前景广阔，但由于涉及从器件物理、电路设计、系统架构到人工智能算法的多个层面存在陡峭的学习曲线，电子-光子人工智能系统的设计与部署仍然极具挑战性。成熟的电子-光子设计自动化工具链的缺失，导致了漫长且低效的设计周期，并限制了跨学科创新与协同演进。在本工作中，我们提出了一个旨在民主化光子人工智能系统开发的跨层协同设计与自动化框架。我们首先介绍了面向可扩展光子边缘人工智能和Transformer推理的架构设计，随后介绍了SimPhony——一个用于快速评估和设计空间探索电子-光子集成电路人工智能系统的开源建模工具。接着，我们重点阐述了人工智能赋能的光子设计自动化方面的进展，包括基于物理人工智能的麦克斯韦方程求解器、一个考虑制造工艺的反向设计框架，以及一种用于元光学神经网络的可扩展反向训练算法，从而为下一代电子-光子人工智能系统构建了一个可扩展的电子-光子设计自动化技术栈。