Hybrid Quantum-Classical Machine Learning for Lithography Hotspot Detection

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.