DPRoute: Deep Learning Framework for Package Routing

Yeu Haw Yeh, Simon Yi Hung Chen, Hung Ming Chen, Deng Yao Tu, Guan Qi Fang, Yun Chih Kuo, Po Yang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


For routing closures in package designs, net order is critical due to complex design rules and severe wire congestion. However, existing solutions are deliberatively designed using heuristics and are difficult to adapt to different design requirements unless updating the algorithm. This work presents a novel deep learning-based routing framework that can keep improving by accumulating data to accommodate increasingly complex design requirements. Based on the initial routing results, we apply deep learning to concurrent detailed routing to deal with the problem of net ordering decisions. We use multi-agent deep reinforcement learning to learn routing schedules between nets. We regard each net as an agent, which needs to consider the actions of other agents while making pathing decisions to avoid routing conflict. Experimental results on industrial package design show that the proposed framework can improve the number of design rule violations by 99.5% and the wirelength by 2.9% for initial routing.

Original languageEnglish
Title of host publicationASP-DAC 2023 - 28th Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781450397834
StatePublished - 16 Jan 2023
Event28th Asia and South Pacific Design Automation Conference, ASP-DAC 2023 - Tokyo, Japan
Duration: 16 Jan 202319 Jan 2023

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC


Conference28th Asia and South Pacific Design Automation Conference, ASP-DAC 2023


  • deep learning
  • multi-agent reinforcement learning
  • substrate routing


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