Automating Analog Constraint Extraction: From Heuristics to Learning: (Invited Paper)
Automating Analog Constraint Extraction: From Heuristics to Learning: (Invited Paper)
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DOI：
10.1109/asp-dac52403.2022.9712488
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Keren Zhu;Hao Chen;Mingjie Liu;D. Pan
中科院分区：
文献类型：
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作者：
Keren Zhu;Hao Chen;Mingjie Liu;D. Pan
模拟布局综合最近受到了广泛关注，以减轻手动布局工作不断增加的成本。为了实现所需的性能和设计规范，生成布局约束对于全自动网表到 GDSII 模拟布局流程至关重要。然而，模拟布局综合中的自动约束提取和约束管理之间存在很大差距。本文介绍了模拟布局综合现有的约束类型，并指出了自动化模拟约束提取的最新研究趋势。具体来说，本文回顾了传统的图启发式方法，例如图相似性和最近利用图神经网络的机器学习方法。它还讨论了挑战和研究机会。
Analog layout synthesis has recently received much attention to mitigate the increasing cost of manual layout efforts. To achieve the desired performance and design specifications, generating layout constraints is critical in fully automated netlist-to-GDSII analog layout flow. However, there is a big gap between automatic constraint extraction and constraint management in analog layout synthesis. This paper introduces the existing constraint types for analog layout synthesis and points out the recent research trends in automating analog constraint extraction. Specifically, the paper reviews the conventional graph heuristic methods such as graph similarity and the recent machine learning approach leveraging graph neural networks. It also discusses challenges and research opportunities.