The rising demand for AI training and inference, as well as scientific computing, combined with stringent latency and energy budgets, is driving the adoption of integrated photonics for computing, sensing, and communications. As active photonic integrated circuits (PICs) scale in device count and functional heterogeneity, physical implementation by manual scripting and ad-hoc edits is no longer tenable. This creates an immediate need for an electronic–photonic design automation (EPDA) stack in which physical design automation is a core capability. However, there is currently no end-to-end fully automated routing flow that coordinates photonic waveguides and on-chip metal interconnect. Critically, available digital VLSI and analog/custom routers are not directly applicable to PIC metal routing due to a lack of customization to handle constraints induced by photonic devices and waveguides. We present, to our knowledge, the first end-to-end routing framework LiDAR 3.0 for large-scale active PICs that addresses waveguides and metal wires within a unified flow. We introduce a physically-aware global planner that generates congestion- and crossing-aware routing guides while explicitly accounting for the region of photonic components and waveguides. We further propose a sequence-consistent track assignment and a soft guidance-assisted detailed routing to speed up the routing process with significantly optimized routability and via usage. Evaluated on various large PIC designs, our router delivers fast, high-quality active PIC routing solutions with fewer vias, lower congestion, and competitive runtime relative to manual and existing VLSI router baselines; on average it reduce via count by ~99%, user-specified design rule violation by ~98%, and runtime by 17x, establishing a practical foundation for EPDA at system scale.

Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. Most PTC designs today are manually constructed, with low design efficiency and unsatisfying solution quality. This makes it challenging to meet various hardware specifications and keep up with rapidly evolving AI applications. Prior work has explored gradient-based methods to learn a good PTC structure differentiably. However, it suffers from slow training speed and optimization difficulty when handling multiple non-differentiable objectives and constraints. Therefore, in this work, we propose a more flexible and efficient zero-shot multi-objective evolutionary topology search framework ADEPT-Z that explores Pareto-optimal PTC designs with advanced devices in a larger search space. Multiple objectives can be co-optimized while honoring complicated hardware constraints. With only <3 hours of search, we can obtain tens of diverse Pareto-optimal solutions, 100× faster than the prior gradient-based method, outperforming prior manual designs with 2× higher accuracy weighted area-energy efficiency. The code of ADEPT-Z is available at link.

Electronic-photonic integrated circuits (EPICs) offer transformative potential for next-generation high-performance AI, but they require interdisciplinary advances across devices, circuits, architecture, and design automation. The complexity of these hybrid systems makes it challenging even for domain experts to understand distinct behaviors and interactions across the design stack. The lack of a flexible, accurate, fast, and easy-to-use EPIC AI system simulation framework significantly limits the exploration of hardware innovations and system evaluations on common benchmarks. To address this gap, we propose SimPhony, a cross-layer modeling and simulation framework for heterogeneous electronic-photonic AI systems. SimPhony offers a platform that enables (1) generic, extensible hardware topology representation that supports heterogeneous multi-core architectures with diverse photonic tensor core designs; (2) optics-specific dataflow modeling with unique multi-dimensional parallelism and reuse beyond spatial/temporal dimensions; (3) data-aware energy modeling with realistic device responses, layout-aware area estimation, link budget analysis, and bandwidth-adaptive memory modeling; and (4) seamless integration with model training framework for hardware/software co-simulation. By providing a unified, versatile, and high-fidelity simulation platform, SimPhony enables researchers to innovate and evaluate EPIC AI hardware across multiple domains, facilitating the next leap in emerging AI hardware. Our code is open-sourced at link1.1https://github.com/ScopeX-ASU/SimPhony

Nanophotonic device design aims to optimize pho-tonic structures to meet specific requirements across various applications. Inverse design has unlocked non-intuitive, high-dimensional design spaces, enabling the discovery of compact, high-performance device topologies beyond traditional heuristic or analytic methods. The adjoint method, which calculates analytical gradients for all design variables using just two electromagnetic simulations, enables efficient navigation of this complex space. However, many inverse-designed structures, while numerically plausible, are difficult to fabricate and highly sensitive to physical variations, limiting their practical use. The discrete material distributions with numerous local-optimal structures also pose significant optimization challenges, often causing gradient-based methods to converge on suboptimal designs. In this work, we formulate inverse design as a fabrication-restricted, discrete, prob-abilistic optimization problem and introduce BOSON−1, an end-to-end, adaptive, variation-aware subspace optimization framework to address the challenges of manufacturability, robustness, and optimizability. We explicitly consider the fabrication process and differentiably optimize the design in the fabricable subspace. To overcome optimization difficulty, we propose dense target-enhanced gradient flows to mitigate misleading local optima and introduce a conditional subspace optimization strategy to create high-dimensional tunnels to escape local optima. Furthermore, we significantly reduce the prohibitive runtime associated with optimizing across exponential variation samples through an adaptive sampling-based robust optimization method, ensuring both efficiency and variation robustness. On three representative photonic device benchmarks, our proposed inverse design methodology BOSON−1delivers fabricable structures and achieves the best convergence and performance under realistic variations, outperforming prior arts with 74.3% post-fabrication performance.

Inverse design has emerged as a transformative approach for photonic device optimization, enabling exploration of high-dimensional, non-intuitive design spaces to create ultra-compact, high-performance devices, advancing photonic inte-grated circuits (PICs) in computing and interconnects. However, practical challenges, such as suboptimal device performance compared to manual designs, 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 bench-mark 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. MAP S features three synergistic components: 1 MAPS-Data: A dataset acquisition framework for generating multi-fidelity, richly labeled device designs using intelligent sampling strategies, providing high-quality data for AI-for-optics research. 2 MAPS-Train: A flexible AI-for-photonics training framework, offering hierarchical data loading pipeline, customizable model construction, support for data- and physics-driven losses, and comprehensive evaluation metrics. 3 MAPS-InvDes: An advanced adjoint method-based inverse design toolkit that abstracts complex physics but exposes flexible optimization steps, integrates pre-trained AI models, and incorporates fabrication-aware variation models, for real-world applicability. 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.