Titlebook: Machine Learning Applications in Electronic Design Automation; Haoxing Ren,Jiang Hu Book 2022 The Editor(s) (if applicable) and The Author

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critic

Machine Learning for Analog Circuit Sizingis also presented. We then review and analyze several recently proposed methods on analog sizing, highlighting the adoption of ML techniques. Finally, we summarize the challenges and opportunities in applying ML for analog circuit sizing problem.

柳树；枯黄

Net-Based Machine Learning-Aided Approaches for Timing and Crosstalk Predictionsive review of net-based ML-aided approaches for timing and crosstalk prediction. Then, four representative case studies are introduced in detail with the focus on problem formulation, prediction flow, feature engineering, and machine learning engines. Finally, a few conclusion remarks are given.

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Anguish

LOPE

Machine Learning for Testability Predictioncal machine learning approaches for testability measurements, which focuses on a set of testability-related prediction problems in both component level and circuit level. In addition, several considerations on applying machine learning models for practical testability improvement are introduced.

octogenarian

RL for Placement and Partitioningn overview of deep RL, a primer on how to formulate chip placement as a deep RL problem, and a detailed description of a recent RL-based approach to chip placement. The chapter concludes with a discussion of other applications for RL-based methods and their implications for the future of chip design.

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兽群

Machine Learning for Mask Synthesis and Verification using machine learning for mask synthesis and verification, including lithograph modeling, hotspot detection, mask optimization, and layout pattern generation. We hope this chapter can motivate future research on AI-assisted DFM solutions.

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Machine Learning Applications in Electronic Design Automation