  With the rapid development of artificial intelligence and autonomous driving
technology, the demand for semiconductors is projected to rise substantially.
However, the massive expansion of semiconductor manufacturing and the
development of new technology will bring many defect wafers. If these defect
wafers have not been correctly inspected, the ineffective semiconductor
processing on these defect wafers will cause additional impact to our
environment, such as excessive carbon dioxide emission and energy consumption.
In this paper, we utilize the information processing advantages of quantum
computing to promote the defect learning defect review (DLDR). We propose a
classical-quantum hybrid algorithm for deep learning on near-term quantum
processors. By tuning parameters implemented on it, quantum circuit driven by
our framework learns a given DLDR task, include of wafer defect map
classification, defect pattern classification, and hotspot detection. In
addition, we explore parametrized quantum circuits with different
expressibility and entangling capacities. These results can be used to build a
future roadmap to develop circuit-based quantum deep learning for semiconductor
defect detection.
