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defect classification for Additive Manufacturing with Machine Learning

MATERIALS 2023年第18期16卷 6242-6242页

作者： Altmann, Mika Leon Benthien, Thiemo Ellendt, Nils Toenjes, Anastasiya Leibniz Inst Mat Engn IWT Badgasteiner Str 3 D-28359 Bremen Germany Univ Bremen Fac Prod Engn Badgasteiner Str 1 D-28359 Bremen Germany

Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model's accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process's parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.

关键词： PBF-LB/M Ti6Al4V defect classification machine learning additive manufacturing

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Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT-LCD panels

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JOURNAL OF INTELLIGENT MANUFACTURING 2022年第6期33卷 1747-1759页

作者： Lee, Minyoung Jeon, Joohyoung Lee, Hongchul Korea Univ Dept Ind Management Engn Seoul South Korea

The deep learning (DL) model has performed successfully in various fields, including manufacturing. DL models for defect image data analysis in the manufacturing field have been applied to multiple domains such as defect detection, classification, and localization. However, DL models require trade-offs in accuracy and interpretability. We use explainable artificial intelligence techniques to analyze the predicted results of the defect image classification model, which is considered as a "black-box" model, to produce human-understandable results. We visualize defects using layer-wise relevance propagation-based methods, fit the model into a decision tree, and convert prediction results into human-interpretable text. Our research complements the interpretation of prediction results for the classification model. The domain expert can obtain the reliability and explanatory ability for the defect classification of TFT-LCD panel data of the DL model through the results of the proposed analysis.

关键词： Deep learning defect classification Visualization Explainable artificial intelligence

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SEMI-PointRend: Improved Semiconductor Wafer defect classification and Segmentation as Rendering 37

SEMI-PointRend: Improved Semiconductor Wafer Defect Classifi...

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Conference on Metrology, Inspection, and Process Control XXXVII

作者： Hwang, MinJin Dey, Bappaditya Dehaerne, Enrique Halder, Sandip Shin, Young-Han imec Kapeldreef 75 B-3001 Leuven Belgium Univ Ulsan Dept Phys Ulsan South Korea Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (纸本)9781510660991;9781510661004

In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by image rendering in computer graphics, a new image segmentation method that can generate high-resolution segmentation masks. It can also be flexibly integrated into common instance segmentation meta-architecture such as Mask-RCNN and semantic meta-architecture such as FCN. We implemented a model, termed as SEMI-PointRend, to generate precise segmentation masks by applying the PointRend neural network module. In this paper, we focus on comparing the defect segmentation predictions of SEMI-PointRend and Mask-RCNN for various defect types (line-collapse, single bridge, thin bridge, multi bridge non-horizontal). We show that SEMI-PointRend outperforms Mask R-CNN by up to 18.8% in terms of segmentation mean average precision.

关键词： semiconductor defect inspection metrology lithography stochastic defects supervised learning deep learning defect classification defect localization defect mask segmentation mask r-cnn pointrend

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Bolt defect classification algorithm based on knowledge graph and feature fusion

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ENERGY REPORTS 2022年 8卷 856-863页

作者： Kong, Yinghui Liu, Xu Zhao, Zhenbing Zhang, Dongxia Duan, Jikun North China Elect Power Univ Dept Elect & Commun Engn Baoding 071003 Hebei Peoples R China North China Elect Power Univ Hebei Key Lab Power Internet Things Technol Baoding 071003 Hebei Peoples R China China Elect Power Res Inst Beijing 100192 Haidian Distric Peoples R China

At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively;Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Bolt and nut pair Knowledge graph Feature fusion Decision fusion defect classification

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Robust Adversarial Training for Industrial defect classification with Long-Tailed Data

Robust Adversarial Training for Industrial Defect Classifica...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Shuchun Lyu, Jiguang Man, Dapeng Xiong, Hengheng Liu, Tao Yang, Wu College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350368741

Deep neural networks are vulnerable to adversarial examples which fool model predictions by adding imperceptible perturbations to natural examples. Adversarial training is effective in defending against adversarial attacks but faces a challenge with long-tailed data, where the over-compression of tail feature space undermines the reliability of defect classification models. To address this problem, we propose APRCB-AT, a novel adversarial training framework that integrates an adaptive perturbation radius and class-balanced loss. We find a positive correlation between the aggressiveness of adversarial examples and the perturbation radius within a certain range. Based on this, APRCB-AT assigns a larger perturbation radius in a certain range to the tail classes, while incorporating class-balanced loss with regularization to penalize the head classes. The experimental results show that APRCB-AT achieves 70.28% robust accuracy against adversarial attacks, surpassing existing methods, such as LSRG-DRW(67.22%) and REAT(68.06%). © 2025 IEEE.

关键词： Adversarial Attack Adversarial Robustness Deep Learning defect classification Long-Tailed Distribution

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Optimal Feature Selection for defect classification in Semiconductor Wafers

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 324-331页

作者： Gomez-Sirvent, Jose L. de la Rosa, Francisco Lopez Sanchez-Reolid, Roberto Fernandez-Caballero, Antonio Morales, Rafael Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informat Albacete 02071 Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac Albacete 02071 Spain

Semiconductors are essential components in many electronic devices. Because wafers are produced quickly and in large quantities, defects occur that adversely affect semiconductor properties. This makes it necessary to install powerful and robust inspection systems which use artificial intelligence techniques in the early stages of the manufacturing chain in order to detect and classify those defects. This paper proposes a method for defect detection and classification on images of semiconductor wafer materials obtained by means of a scanning electron microscope based in the following stages: (i) use of computer vision techniques to isolate the defect from the background;(ii) use of several descriptors based on shape, size, texture, histogram, and key-points to create a feature vector for the characterization of the defect;(iii) application of an exhaustive search as a feature selection method to determine the optimal subset of feature descriptors;and (iv) evaluation of the feature descriptors by using a support vector machine classifier providing the optimal set with highest F1-score metrics. Finally, the effectiveness of the proposed approach is compared with five popular feature selection methods, reporting better classification results than the latter.

关键词： Feature extraction Support vector machines Analysis of variance Computer vision Scanning electron microscopy Manufacturing Inspection Semiconductor manufacturing defect classification feature selection exhaustive search machine learning support vector machine

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A New defect classification Approach Based on the Fusion Matrix of Multi-Eigenvalue

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IEEE SENSORS JOURNAL 2021年第3期21卷 3398-3407页

作者： Lei, Biting Yi, Pengxing Xiang, Jiayun Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

defect recognition plays an important part in the health monitoring of in-service equipment. Surface defects and sub-surface defects of key components have different effects on the safety of the equipment. Sub-surface defects are difficult to be identified, and it is more likely to cause unpredictable damage than surface defects. However, the existing characteristic cannot accurately identify the defect type. Therefore, it is critical to find features that are useful for defect classification. The fusion of multiple feature values facilitates defect classification. For obtaining multi-feature fusion eigenvalues, this article introduces a fusion matrix based on Fisher's discriminant criterion and correlation analysis, which realizes the fusion of two dimensions of the whole and the local and different aspects. The BP neural network classifier is then used to discriminate and classify surface defects and sub-surface defects. The recognition accuracy of surface defects and sub-surface defects can reach 98.1%. The classification accuracy of this approach is significantly improved compared with the method based on a single feature value and other state-of-the-art methods. The approach can also be used to identify defects with different cross-sectional shapes, indicating that this approach is suited for nature defects.

关键词： defect classification fused matrix fisher's discriminant criterion the correlation coefficient

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defect classification on semiconductor wafers using Fisher vector and visual vocabularies coding

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MEASUREMENT 2022年 202卷

作者： Gomez-Sirvent, Jose L. Lopez de la Rosa, Francisco Sanchez-Reolid, Roberto Morales, Rafael Fernandez-Caballero, Antonio Inst Invest Informat I3A Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informat Albacete 02071 Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac Albacete 02071 Spain

The main trend in the classification of defects in semiconductor wafers from scanning electron microscopy images is the use of convolutional neural networks. These methods allow automatic feature extraction and supply high accuracy. However, they have limitations since they need a large number of samples in the training phase and are computationally expensive in general. Methods based on visual vocabularies are an efficient alternative to this type of networks that can be implemented with a reduced number of training samples. Although visual vocabularies have been successfully used in different image classification problems, to our knowledge they have never been tested in the classification of defects in semiconductor material wafers from scanning electron microscopy images. On the rectangular region holding the defect, the scale-invariant feature transform algorithm is applied to determine the points of interest and to calculate the descriptors of the patches containing these points. By training a clustering algorithm with these features, a visual vocabulary is created to describe each image as a bag of visual words. Two approaches for coding the descriptors are studied: (i) The more traditional one performs a count of the occurrence of each visual word, and (ii) a modern approach known as Fisher vector which measures the deviation between each descriptor and the generative Gaussian mixture model. Comparing both variants using support vector machines it is seen that Fisher vector coding is computationally more efficient and reports better classification results. The usefulness of visual vocabularies for defect classification on semiconductor wafers is showed by the 91.89% and 93.13% accuracy obtained for bag of visual words and Fisher vector coding, respectively. The training and testing times are lower than 5 min for both approaches.

关键词： defect classification Bag of Visual Words Fisher vector Semiconductor manufacturing

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Automatic defect classification for Infrared Thermography in CFRP based on Deep Learning Dense Convolutional Neural Network

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2024年第3期43卷 73-73页

作者： Liu, Guozeng Gao, Weicheng Liu, Wei Chen, Yijiao Wang, Tianlong Xie, Yongzhi Bai, Weiliang Li, Zijing Harbin Inst Technol Dept Astronaut Sci & Mech Harbin 150001 Peoples R China Harbin Univ Commerce Coll Light Ind Harbin 150028 Peoples R China

Carbon fiber reinforced polymer (CFRP) is an important composite material widely used in aerospace and other industries. However, long-term service in harsh environments can lead to various defects such as debonding, delamination, water, cracks, etc. Therefore, it becomes imperative to conduct non-destructive testing (NDT) on CFRP to ensure its structural integrity and safety. Infrared thermography was employed for defect classification in CFRP laminate and CFRP honeycomb sandwich composites (HSC) by applied a convolutional neural networks (CNN). The proposed automatic defect classification method based on CNN is one of the goals of NDE 4.0 to apply advanced technologies (such as deep learning and AI) to improve NDT efficiency and accuracy. The infrared detection dataset consisted of five classes: debonding, water, delamination, crack, and health. To effectively expand the dataset, offline data augmentation technique were employed. A deep learning technique of Dense convolutional neural network (DCNN) were proposed to defect classification. AlexNet, VGG-16, ResNet-50 and DenseNet-121 based on transfer learning fine-tuning model was applied to classify debonding, water, delamination, crack and health. The classification results were analyzed by using a confusion matrix. The results shown that the accuracy of AlexNet, VGG-16, ResNet-50 and DenseNet-121 were 92.34%, 82.86%, 88.30%, 98.48%, respectively. DenseNet-121 demonstrates good performance in defect detection and recognition with an accuracy of 98.48%, and DenseNet-121 has high application potential in accurately classify and recognize defects in deep learning technique.

关键词： Infrared thermography Dense convolutional neural networks (DCNN) defect classification Deep learning NDT 4.0

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Nuisance Rate Improvement of E-beam defect classification 33

Nuisance Rate Improvement of E-beam Defect Classification

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33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

作者： Lei, Hairong Dong, Qian Teh, Cho Pu, Lingling Jen, Chih-Yu Lin, Steve ASML San Jose CA 95131 USA

ISBN: (纸本)9781665494878

The proposed paper presents a case study describing how e-beam defect classification nuisance rate (NR) can be improved by the implementation of a new machine learning classification process in HMI eManager even for difficult data (feature boundary is overlay). This is important because low nuisance rate is an importance metric to measure the e-beam defect classification performance and it is usually difficult to obtain the low nuisance rate, especially for difficult defect dataset. Our machine learning (not a deep learning) multiple-phase classification results show that it is an effective way to improve the E-beam defect classification nuisance rate.

关键词： defect classification Nuisance rate Multi-phase classification Machine learning E-beam

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