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.

本文提出使用量子计算技术的经典量子混合算法来进行近期量子处理器上的深度学习，以实现损伤芯片的缺陷学习和缺陷检查，保护环境免受二氧化碳排放和能源消耗的影响。同时探讨使用不同表达能力和纠缠容量的参数化量子电路，可用于构建一条未来的路线图，将基于电路的量子深度学习应用于半导体缺陷检测。

混合经典量子深度学习在半导体缺陷检测中的应用

Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning

Statistical learning of materials properties or functions so far starts with
a largely silent, non-challenged step: the choice of the set of descriptive
parameters (termed descriptor). However, when the scientific connection between
the descriptor and the actuating mechanisms is unclear, causality of the
learned descriptor-property relation is uncertain. Thus, trustful prediction of
new promising materials, identification of anomalies, and scientific
advancement are doubtful. We analyse this issue and define requirements for a
suited descriptor. For a classical example, the energy difference of
zincblende/wurtzite and rocksalt semiconductors, we demonstrate how a
meaningful descriptor can be found systematically.

通过分析材料学中的描述符选择问题，定义了一个合适的描述符要求，并以典型半导体结构的能量差为例，演示了如何系统地找到有意义的描述符。