TY - GEN
T1 - Hybrid Quantum-Classical Machine Learning for Lithography Hotspot Detection
AU - Yang, Yuan Fu
AU - Sun, Min
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - LIT Hotspot
KW - Quantum Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85132820420&partnerID=8YFLogxK
U2 - 10.1109/ASMC54647.2022.9792509
DO - 10.1109/ASMC54647.2022.9792509
M3 - Conference contribution
AN - SCOPUS:85132820420
T3 - ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
BT - 2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2022
Y2 - 2 May 2022 through 5 May 2022
ER -