Titlebook: Machine Learning in VLSI Computer-Aided Design; Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin Li Book 2019 Springer Nature Switzerland AG 2

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Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin LiProvides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability.Discusses the use of machine learn

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Springer Nature Switzerland AG 2019

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A Preliminary Taxonomy for Machine Learning in VLSI CAD,ception. The purpose of this book is to bring to the interested reader a cross-section of the connections between existing and emerging machine learning methods and VLSI computer aided design (CAD). In this brief introduction, we begin with a high-level taxonomy of machine learning methods. We then

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Machine Learning in Physical Verification, Mask Synthesis, and Physical Design as physical design, mask synthesis, and physical verification are critical to guarantee fast design closure and manufacturability. Recent advances in machine learning provide various new opportunities and approaches to tackle these challenges. This chapter will discuss several applications of machi

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Machine Learning Approaches for IC Manufacturing Yield Enhancementinterest in machine learning and data mining techniques to improve yield to take advantage of this increasing volume of data. In this chapter, we introduce machine learning yield models for integrated circuit (IC) manufacturing yield enhancement. Challenges in this area include class imbalance due t