Re-examining VLSI Manufacturing and Yield through the Lens of Deep Learning : (Invited Talk)

Mohamed Baker Alawieh, Wei Ye, David Z. Pan

Abstract: The continued scaling of integrated circuit technologies, along with the increased design complexity, has exacerbated the challenges associated with manufacturability and yield. In today's semiconductor manufacturing, lithography plays a fundamental role in printing design patterns on silicon. However, the growing complexity and variation of the manufacturing process have tremendously increased the lithography modeling and simulation cost. Besides, both the role and cost of resolution enhancement techniques (RETs) - now indispensable in the design process - have increased. Parallel to these developments are the recent advancements in Machine Learning (ML) which have provided a far-reaching data-driven perspective for problem solving. In this work, we shed light on the recent Deep Learning (DL) based approaches that have provided a new lens to examine traditional manufacturability and yield challenges. We present lithography modeling and simulation techniques, leveraging advanced learning paradigms, which have demonstrated unprecedented efficiency. Moreover, we demonstrate the role DL can play in advancing RETs by presenting its successful application in assist feature generation. Also critical to yield is the post fabrication wafer map defect analysis step which our work tackles using a novel confidence-aware deep learning scheme. This paper further discusses the future prospects of DL-based approaches in the scope of circuits manufacturability and yield.