LiDAR 2.0: Hierarchical Curvy Waveguide Detailed Routing for Large-Scale Photonic Integrated Circuits

TL;DR

LiDAR2.0 addresses the challenge of scalable, DRV-free detailed routing for large-scale PICs by introducing a hierarchical, curvy-aware A* routing engine that accommodates non-Manhattan waveguides, adaptive crossing insertion, and congestion-aware net ordering. The framework is extended with hierarchical routing, offset-neighbor search, and crossing-space preservation, enabling efficient reuse of subcircuit layouts and improved conflict resilience. Open-source PIC intermediate representation and benchmark suites support realistic evaluation, with LiDAR2.0 achieving up to $IL_{max}$ reductions of 16% in spacious layouts and 9% in compact ones, along with up to 7.69x and 6.95x speedups over prior methods and LiDAR1.0 respectively. These contributions enable scalable, automated PIC design for large-scale photonic systems, accelerating development and reducing post-routing fixes."

Abstract

Driven by innovations in photonic computing and interconnects, photonic integrated circuit (PIC) designs advance and grow in complexity. Traditional manual physical design processes have become increasingly cumbersome. Available PIC layout tools are mostly schematic-driven, which has not alleviated the burden of manual waveguide planning and layout drawing. Previous research in PIC automated routing is largely adapted from electronic design, focusing on high-level planning and overlooking photonic-specific constraints such as curvy waveguides, bending, and port alignment. As a result, they fail to scale and cannot generate DRV-free layouts, highlighting the need for dedicated electronic-photonic design automation tools to streamline PIC physical design. In this work, we present LiDAR, the first automated PIC detailed router for large-scale designs. It features a grid-based, curvy-aware A* engine with adaptive crossing insertion, congestion-aware net ordering, and insertion-loss optimization. To enable routing in more compact and complex designs, we further extend our router to hierarchical routing as LiDAR 2.0. It introduces redundant-bend elimination, crossing space preservation, and routing order refinement for improved conflict resilience. We also develop and open-source a YAML-based PIC intermediate representation and diverse benchmarks, including TeMPO, GWOR, and Bennes, which feature hierarchical structures and high crossing densities. Evaluations across various benchmarks show that LiDAR 2.0 consistently produces DRV-free layouts, achieving up to 16% lower insertion loss and 7.69x speedup over prior methods on spacious cases, and 9% lower insertion loss with 6.95x speedup over LiDAR 1.0 on compact cases. Our codes are open-sourced at https://github.com/ScopeX-ASU/LiDAR.

Figures (17)

• Figure 1: Modern PIC scale and complexity require EPDA.
• Figure 2: Compare properties/rules of EIC and PIC routing.
• Figure 3: Algorithm flow of our LiDAR framework.
• Figure 4: (a) Port propagation and reserved port region help port access. (b) Port spreading removes congested ports in the same grid. (c) group-based net order with access point offset enables channel planning and allows potential crossing.
• Figure 5: Parametric curvy-aware neighbors allow non-Manhattan curvy waveguide routing. Neighbors are automatically derived based on bending radius and grid size.