arXiv:2208.03514 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning
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.
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.
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.
The quality of raw materials is fundamental to the reliability and overall performance of final products, serving as the cornerstone of modern manufacturing standards. While traditional inspection methods can be effective, they are frequently time-consuming, labor-intensive, and unsuitable for high-throughput production environments. Recent advances in quantum computing offer significant advantages for processing large-scale data and complex feature representations, with strong potential to address the limitations of traditional inspection techniques. This study introduces an automated approach for grading raw materials, such as steel and fabric, which are widely used in industries including automotive, textiles, and general manufacturing, by integrating deep learning with quantum machine learning. A Hybrid Quantum Classical Neural Network (HQCNN) is proposed to enhance defect detection. The HQCNN utilizes a fine-tuned ResNet-18 backbone for robust feature extraction. Extracted features are used by a trainable depth controller that determines the complexity of a six-qubit variational quantum circuit (VQC) with data-adaptive, dynamic depth. Each VQC layer consists of rotations around the Y -axis (RY) and Z -axis (RZ), combined with Controlled-NOT (CNOT) gates arranged in a ring topology, with the total number of layers determined on a per-sample basis. The model is trained using a resource-aware loss function that penalizes excessive circuit depth, and the resulting quantum features are fused with classical features for final classification. Comprehensive ablation studies comparing the HQCNN against parameter-matched classical multi-layer perceptrons (MLPs) and fixed-depth quantum circuits demonstrate the fundamental advantage of the proposed approach. The dynamic HQCNN achieves 88.1 ± 2.4% accuracy on the complex metal surface defect dataset and 93.0 ± 1.8% on the fabric defects dataset. More importantly, it yields a statistically significant improvement in the macro F1-score (from 0.79 in classical parameter-matched baselines to 0.87), proving its superior capability in recognizing complex, minority-class defects while utilizing a fraction of the trainable parameters in the classification head compared to traditional architectures.
Abstract Accurate classification of wafer map defects is crucial for enhancing semiconductor yield and diagnosing process irregularities. Convolutional neural networks (CNNs) have demonstrated impressive performance on the WM-811K wafer map dataset; nevertheless, as structures become more complex, their processing requirements increase. The possibility for more compact representations and effective training through quantum parallelism is shown by recent developments in quantum machine learning (QML), namely quantum neural networks and quantum convolutional models. Inspired by these advancements, this work proposes a hybrid framework for wafer defect classification that combines a Quantum Convolutional Neural Network (QCNN) with a classical CNN (CCNN). As far as we are aware, this is the first time a QCNN has been used for semiconductor wafer map analysis. Experimental results reveal that while the QCNN achieves accuracy ( R 2 = 99.38%) comparable to the CCNN ( R 2 = 99.32%), it greatly decreases training time due to the low-overhead Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer and the compactness of quantum circuits. These results demonstrate the potential of quantum-enhanced architectures for semiconductor analytics in the future as quantum hardware develops.
As manufacturing systems advance toward higher throughput, tighter tolerances, and increasingly complex quality requirements, traditional defect detection and quality assurance methods struggle to scale effectively. Quantum Machine Learning (QML) offers a new computational paradigm capable of capturing high-dimensional feature interactions, accelerating pattern recognition, and improving anomaly detection in industrial environments. This study investigates the integration of quantum kernel methods, variational quantum classifiers, and hybrid quantum–classical neural architectures for real-time defect identification across optical, acoustic, and sensor-rich inspection systems. The proposed framework leverages the expressivity of quantum feature spaces to enhance classification fidelity while maintaining compatibility with existing digital manufacturing pipelines. Experimental simulations demonstrate that QML models achieve notable improvements in detecting subtle surface irregularities, micro-defects, and non-linear process deviations when compared to conventional deep learning baselines. These findings underscore the potential of quantum-enhanced analytics to redefine quality assurance strategies in high-throughput manufacturing, enabling earlier defect localization, lower false-negative rates, and more resilient production workflows.
Processing Digital media such images is a crucial step for any image classification task such as surface defect detection or any deep learning task which include Digital Media Processing. Ensuring the detail capturing of each and every feature of the input image from each and every pixel is must in order to build a robust system with minimal error or no error. While it may not be possible and causes the struggle for classical computing methods to efficiently capture and process complex patterns and subtle details. Utilising quantum computing for digital image processing gives an advantage over classical methods as it operates on high dimensional space and leverages quantum parallelism to explore numerous possibilities simultaneously. This enable the extraction of more complex features that might be otherwise missed improving the model’s capability to flag defects with greater accuracy and efficiency. Quantum computing’s potential to enhance feature extraction can significantly boost the performance of classification models, leading to dependable and precise surface defect detection.
The entwining of quantum computing with classical machine learning has proven to be one of the most promising frontiers of artificial intelligence, as it possesses the potential to speed up intricate computations in ways that classical systems will never be able to achieve. This study delves into the theoretical and practical implications of hybrid quantum-classical approaches in machine learning, while also examining their advantages, limitations, and applicability in different domains. Utilizing quantum entanglement and parallelism, hybrid solutions also have the potential to process high-dimensional data, optimize complex functions, and facilitate pattern recognition tasks more efficiently. However, noise, decoherence, and limited qubit connectivity on current hardware pose considerable barriers to practical implementations. The authors discuss hybrid architectures like quantum-enhanced optimizers and variational quantum circuits which include classical pre- and post-processing to alleviate quantum limitations. Moreover, a comparative study is performed to show the effectiveness of hybrid models comparing with classical machine learning state-of-the-art in natural language processing, computer vision and financial modeling. Results indicate that although hybrid quantum–classical approaches provide significant gains in optimization and computational efficiency; these benefits remain limited by hardware limitations and the scalability of quantum algorithms. With these considerations in mind, the paper offers guidance for targeting future research directions in quantum machine learning, including advances in quantum error correction, quantum hardware scalability, and new algorithmic approaches. Additional advancements needed and notably, the study focused on a smaller representation of data Since quantum computing is relatively new and researchers are still exploring its application, it is still early days in terms of machine learning models that are solely, intrinsically quantum.