# Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning

arXiv: https://arxiv.org/abs/2208.03514 · HTML: https://mlsanity.com/paper/2208.03514/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 6 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems](https://mlsanity.com/paper/2003.00264/) (2020)
- [Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning](https://mlsanity.com/paper/2102.06955/) (2021)
- [Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation](https://mlsanity.com/paper/2107.04367/) (2021)
- [Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks](https://mlsanity.com/paper/2004.12492/) (2020)

## Built on by (most cited first, 16 of 16)

- [Quantum Vision Transformers](https://mlsanity.com/paper/2209.08167/) (2022)
- [Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey](https://mlsanity.com/paper/2402.02941/) (2024)
- [Quantum Multi-Model Fitting](https://mlsanity.com/paper/2303.15444/) (2023)
- [A novel approach for wafer defect pattern classification based on topological data analysis](https://mlsanity.com/paper/2209.08945/) (2022)
- [Quantum Annealing for Single Image Super-Resolution](https://mlsanity.com/paper/2304.08924/) (2023)
- [3D-QAE: Fully Quantum Auto-Encoding of 3D Point Clouds](https://mlsanity.com/paper/2311.05604/) (2023)
- [Robust Fitting on a Gate Quantum Computer](https://mlsanity.com/paper/2409.02006/) (2024)
- [SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation](https://mlsanity.com/paper/2307.08693/) (2023)
- [SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection](https://mlsanity.com/paper/2308.07180/) (2023)
- [SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images](https://mlsanity.com/paper/2303.04737/) (2023)
- [Fourier Series Guided Design of Quantum Convolutional Neural Networks for Enhanced Time Series Forecasting](https://mlsanity.com/paper/2404.15377/) (2024)
- [Semiconductor Wafer Map Defect Classification with Tiny Vision Transformers](https://mlsanity.com/paper/2504.02494/) (2025)
- [Defect Detection Approaches Based on Simulated Reference Image](https://mlsanity.com/paper/2303.11971/) (2023)
- [Decision Fusion Network with Perception Fine-tuning for Defect Classification](https://mlsanity.com/paper/2309.12630/) (2023)
- [Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects](https://mlsanity.com/paper/2309.12641/) (2023)
- [Bring the Power of Diffusion Model to Defect Detection](https://mlsanity.com/paper/2408.13845/) (2024)

## Abstract (arXiv)

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.