LiDAR 2.0: Hierarchical Curvy Waveguide Detailed Routing for Large-Scale Photonic Integrated Circuits
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866911228795813888 |
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| author | Zhou, Hongjian Yang, Haoyu Ying, Ziang Gangi, Nicholas Zhaoran Huang Ren, Haoxing Matres, Joaquin Gu, Jiaqi |
| author_facet | Zhou, Hongjian Yang, Haoyu Ying, Ziang Gangi, Nicholas Zhaoran Huang Ren, Haoxing Matres, Joaquin Gu, Jiaqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiDAR 2.0: Hierarchical Curvy Waveguide Detailed Routing for Large-Scale Photonic Integrated Circuits Zhou, Hongjian Yang, Haoyu Ying, Ziang Gangi, Nicholas Zhaoran Huang Ren, Haoxing Matres, Joaquin Gu, Jiaqi Emerging Technologies Optics 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. |
| title | LiDAR 2.0: Hierarchical Curvy Waveguide Detailed Routing for Large-Scale Photonic Integrated Circuits |
| topic | Emerging Technologies Optics |
| url | https://arxiv.org/abs/2505.17239 |