Hybrid Quantum-Classical Machine Learning for Lithography Hotspot Detection

Yuan Fu Yang, Min Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In advanced semiconductor process technology, lithography hotspot detection has become an essential task in design for manufacturability. The ability to detect and repair lithography hotspots that can affect printability is critical to improving yield and productivity. Machine learning technology has become a powerful tool in a variety of applications, from finance to manufacturing and computer vision. The use of quantum systems to process classical data using machine learning algorithms has created an emerging field of research, namely quantum machine learning (QML). We explore the possibility of converting a novel machine learning model to a hybrid quantum-classical machine learning that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. In addition, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities. Then we compare their training performance to quantify the expected benefits. These results can be used to build a future roadmap to develop circuit-based hybrid quantum-classical machine learning for lithography hotspot detection.

Original languageEnglish
Title of host publication2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665494878
DOIs
StatePublished - 2022
Event33rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2022 - Saratoga Springs, United States
Duration: 2 May 20225 May 2022

Publication series

NameASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
Volume2022-May
ISSN (Print)1078-8743

Conference

Conference33rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2022
Country/TerritoryUnited States
CitySaratoga Springs
Period2/05/225/05/22

Keywords

  • LIT Hotspot
  • Quantum Machine Learning

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