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Creators/Authors contains: "Li, Shaolan"

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  1. ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available April 13, 2026
  2. ; ; ; ; ; ; ; (, IEEE)
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  3. ; ; ; ; ; ; ; ; ; ; et al (, IEEE)
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  4. ; ; ; ; ; ; (, IEEE)
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  5. ; ; ; (, IEEE Transactions on Very Large Scale Integration (VLSI) Systems)
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  6. ; ; ; ; ; (, IEEE Biomedical Circuits and Systems Conference (Bio-CAS))
  7. ; ; ; (, IEEE Journal of Solid-State Circuits)
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  8. ; ; ; ; ; ; ; ; (, IEEE Design & Test)
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  9. ; ; ; ; ; ; ; (, 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD))
    Despite tremendous advancement of digital IC design automation tools over the last few decades, analog IC layout is still heavily manual which is very tedious and error-prone. This paper will first review the history, challenges, and current status of analog IC layout automation. Then, we will present MAGICAL, a human-intelligence inspired, fully-automated analog IC layout system currently being developed under the DARPA IDEA program. It starts from an unannotated netlist, performs automatic layout constraint extraction and device generation, then performs placement and post-placement optimization, followed by routing to obtain the final GDSII layout. Various analytical, heuristic, and machine learning algorithms will be discussed. MAGICAL has obtained promising preliminary results. We will conclude the paper with further discussions on challenges and future directions for fully-automated analog IC layout. 
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  10. ; ; ; ; ; ; ; (, 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD))
    Due to sensitive layout-dependent effects and varied performance metrics, analog routing automation for performance-driven layout synthesis is difficult to generalize. Existing research has proposed a number of heuristic layout constraints targeting specific performance metrics. However, previous frameworks fail to automatically combine routing with human intelligence. This paper proposes a novel, fully automated, analog routing paradigm that leverages machine learning to provide routing guidance, mimicking the sophisticated manual layout approaches. Experiments show that the proposed methodology obtains significant improvements over existing techniques and achieves competitive performance to manual layouts while being capable of generalizing to circuits of different functionality. 
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