Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning

TL;DR

This paper introduces a hybrid classical-quantum deep learning approach for semiconductor defect detection, leveraging quantum computing to improve wafer defect classification and hotspot detection, aiming to enhance inspection accuracy and efficiency.

Contribution

It presents a novel hybrid quantum-classical algorithm for semiconductor defect detection, exploring quantum circuit parametrization and its application to real-world defect classification tasks.

Findings

Quantum circuit learning improves defect classification accuracy.

Parametrized quantum circuits with different expressibility are effective.

Framework provides a roadmap for future quantum deep learning in semiconductors.

Abstract

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.