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defect classification Using CNN Transfer Learning and Data Augmentation

管理与系统 2022年第2期29卷 223-239页

作者：陳麗妃黃韻雅張麗雪蘇朝墩

With the development of industrial technology, it is common for factories to introduce automatic optical inspection systems to identify defects in production lines. However, there are still some difficulties in training image classification systems, such as unbalanced data and time-consuming labeling. In order to solve these problems, this study proposes an efficient defect classification modeling procedure by using convolutional neural networks, transfer learning, and data augmentation methods. LED lead frame defect images provided by a Taiwanese LED display manufacturer were used to construct the models and analyze model accuracy and training time. Finally, following comparative analysis, this study suggests applying an online data augmentation method to increase the variability of the dataset and then fine-tuning the pre-trained model to learn and classify the defect features; the classification accuracy of the proposed model can be as high as 98%. This result shows that, using the proposed procedure, a factory can quickly establish a defect classification system to monitor the production process.

关键词： CNN Transfer Learning Data Augmentation defect classification

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Automated defect classification In Semiconductor Devices Using Deep Learning Networks

Automated Defect Classification In Semiconductor Devices Usi...

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IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)

作者： Nair, Arya Sukumaran Hoffrogge, Peter Czurratis, Peter Kuehnicke, Elfgard Wolf, Mario PVA Tepla Analyt Syst GmbH Deutschordenstr 38 D-73463 Westhausen Germany TU Bergakad Freiberg D-09599 Freiberg Germany

ISBN: (数字)9781665498159

ISBN: (纸本)9781665498159

More effective Failure Analysis (FA) technologies are required to meet the upcoming challenges in complex semiconductor devices. Because of recent advances in AI (Artificial Intelligence), we can now concentrate our efforts on developing AI-based algorithms for high precision-automated signal interpretation for failure detection in Scanning Acoustic Microscopes (SAM). Typically, flaw detection in ultrasonic data relies heavily on human expertise, and the majority of automated classifications are based on image-based decision algorithms. For defect classification, the image-based ML approach necessitates a large dataset. On signals, the traditional machine learning approach requires manual feature extraction and selection of the best features. DL approaches are commonly used to automate feature learning and classification from raw signals. This paper proposes a method for creating datasets, preprocessing signals, and semi-supervised signal training for defect classification. For performance evaluation, different DL architectures such as 1D CNN, RNN, and hybrid networks were studied. The models were trained to categorize C4 bumps in flip chips into a defect and intact classes. Even with fewer learnable, 1D-CNN with wavelet applied A-Scan as input outperforms other models with an accuracy of up to 99 percent. The model was then validated by destructive analysis on an unknown sample.

关键词： SAM AI Signal Processing Deep-Learning defect classification Automation CNN RNN

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Deep Learning for Semiconductor defect classification 20

Deep Learning for Semiconductor Defect Classification

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20th IEEE International Conference on Industrial Informatics (INDIN)

作者： Sweeney, Terence Coleman, Sonya Kerr, Dermot Ulster Univ Sch Comp Engn & Intelligent Syst Ulster Londonderry North Ireland

ISBN: (数字)9781728175683

ISBN: (纸本)9781728175683

Automated inspection has become a vital part of quality control during semiconductor wafer production. Current processes are focused on finding defects via variation from a 'golden' image using pixel to pixel comparisons or utilization of opaque neural network-based approaches. In this paper we present an approach which uses deep learning methods to classify defects on semiconductor die images and show the experimental steps taken in order to produce a highly accurate system based on previous models.

关键词： defect Detection defect classification Semiconductor wafers Image Processing Deep learning Transfer Learning Convolutional Neural Networks

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Hyperparameter optimization for convolutional neural network by opposite-based particle swarm optimization and an empirical study of photomask defect classification

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Hong, Tzu-Yen Chen, Chin-Chien Natl Taipei Univ Technol Dept Ind Engn & Management Taipei Taiwan

Automated optical inspection (AOI) technology has been widely used for defect detection in various industries including wafer maps and TFT-LCD. Although the implementation of AOI significantly reduce the efforts of manual visual tests, most of the automated were leaving out defect classification for manual inspection. Previous studies indicate that various technologies including statistical methods or machine learning techniques can be used to classify the defect images. Among them, convolutional neural networks (CNN) bring out attention to classification problems. Indeed, determining the suitable choices of the critical hyperparameters is crucial for classification performances. However, most related studies determine the hyperparameters by testing the performance among a few combinations. Despite some previous studies integrating metaheuristics with CNN to improve the selection of hyperparameters, only a few studies focus on AOI defect classification by using CNN with hyperparameter optimization. This study proposed an opposite-based particle swarm optimization with CNN (OPSO-CNN) to deal with the AOI defect classification of semiconductor photomask while considering the hyperparameter optimization of the proposed CNN model, in which opposite-based initialization mechanism was used to generate the initial particles to increase the diversity of the searching space. An empirical study was conducted to evaluate the performances of the proposed OPSO-CNN by the benchmarks. The results have shown the practical viability of the proposed model.

关键词： defect classification Automated optical inspection Semiconductor photomask Convolutional neural network Hyperparameter optimization

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Photovoltaic cell defect classification based on integration of residual-inception network and spatial pyramid pooling in electroluminescence images

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartA期229卷

作者： Acikgoz, Hakan Korkmaz, Deniz Budak, Umit Gaziantep Islam Sci & Technol Univ Fac Engn & Nat Sci Dept Elect & Elect Engn TR-27260 Gaziantep Turkiye Malatya Turgut Ozal Univ Fac Engn & Nat Sci Dept Elect & Elect Engn TR-44210 Malatya Turkiye Bitlis Eren Univ Fac Engn Dept Elect & Elect Engn TR-13100 Bitlis Turkiye

Electroluminescence (EL) imaging provides high spatial resolution and better identifies micro-defects for in-spection of photovoltaic (PV) modules. However, the analysis of EL images could be typically a challenging process due to complex defect patterns and inhomogeneous background structure. In this study, a deep con-volutional neural network (CNN) model using residual connections and spatial pyramid pooling (SPP) is pro-posed for the efficient classification of PV cell defects. The proposed CNN model is built on the Inception-v3 network. In this way, feature maps in inception modules are shared to reuse in deeper layers and the repre-sentation ability of features is enriched with the pooling process of the SPP in different sizes. Due to the imbalanced class distribution, offline data augmentation strategies are applied and network performance is further improved. The proposed method is evaluated on a publicly available dataset of 8 classes, of which 7 classes are defective and one class is defect-free images. In the comparative evaluation, while other approaches give accuracy values between 76.49% and 89.17%, this value is increased to 93.59% with the proposed method. The experimental results show that the proposed method exhibits more accurate and robust classification per-formance compared with other model combinations and CNN models.

关键词： Electroluminescence images Photovoltaic cells defect classification Inception network Spatial pyramid pooling Residual connection

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Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2021年第1期2021卷 1-20页

作者： Liu, Xi Yin, Yongfeng Li, Haifeng Chen, Jiabin Liu, Chang Wang, Shengli Yin, Rui Nanjing Res Inst Elect Technol Nanjing 210013 Peoples R China Beihang Univ Sch Software Beijing 100191 Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Existing software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 similar to 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

关键词： Radar software Software defect defect classification Latent Dirichlet allocation (LDA) topic model

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defect classification of refrigerant compressor using variance estimation of the transfer function between pressure pulsation and shell acceleration

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SMART STRUCTURES AND SYSTEMS 2020年第2期25卷 255-264页

作者： Kim, Yeon-Woo Jeong, Weui-Bong Pusan Natl Univ Dept Mech Engn Busan 609735 South Korea

This paper deals with a defect classification technique that considers the structural characteristics of a refrigerant compressor. First, the pressure pulsation of the refrigerant flowing in the suction pipe of a normal compressor was measured at the same time as the acceleration of the shell surface, and then the transfer function between the two signals was estimated. Next, the frequency-weighted acceleration signals of the defect classification target compressors were generated using the estimated transfer function. The estimation of the variance of the transfer function is presented to formulate the frequency-weighted acceleration signals. The estimated frequency-weighted accelerations were applied to defect classification using frequency-domain features. Experiments were performed using commercial compressors to verify the technique. The results confirmed that it is possible to perform an effective defect classification of the refrigerant compressor by the shell surface acceleration of the compressor. The proposed method could make it possible to improve the total inspection performance for compressors in a mass-production line.

关键词： defect classification linear compressor transfer function frequency analysis variance minimize mass-production inspection FDR

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Exploiting 2D Coordinates as Bayesian Priors for Deep Learning defect classification of SEM Images

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2021年第3期34卷 436-439页

作者： Arena, Simone Bodrov, Yury Carletti, Mattia Gentner, Natalie Maggipinto, Marco Yang, Yao Beghi, Alessandro Kyek, Andreas Susto, Gian Antonio Univ Padua Dept Informat Engn I-35122 Padua Italy Infineon Technol Dresden GmbH D-01099 Dresden Germany Infineon Technol AG D-85579 Neubiberg Germany

Deep Learning approaches have revolutionized in the past decade the field of Computer Vision and, as a consequence, they are having a major impact in Industry 4.0 applications like automatic defect classification. Nevertheless, additional data, beside the image/video itself, is typically never exploited in a defect classification module: this aspect, given the abundance of data in data-intensive manufacturing environments (like semiconductor manufacturing) represents a missed opportunity. In this work we present a use case related to Scanning Electron Microscope (SEM) images where we exploit a Bayesian approach to improve defect classification. We validate our approach on a real-world case study and by employing modern Deep Learning architectures for classification.

关键词： Bayes methods Manufacturing Scanning electron microscopy Task analysis Numerical analysis Deep learning Production Bayesian priors computer vision convolutional neural network deep learning defect classification

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A Self-Supervised Model Based on CutPaste-Mix for Ductile Cast Iron Pipe Surface defect classification

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SENSORS 2023年第19期23卷 8243-8243页

作者： Zhang, Hanxin Sun, Qian Xu, Ke Univ Sci & Technol Beijing Collaborat Innovat Ctr Steel Technol Beijing 100083 Peoples R China

Online surface inspection systems have gradually found applications in industrial settings. However, the manual effort required to sift through a vast amount of data to identify defect images remains costly. This study delves into a self-supervised binary classification algorithm for addressing the task of defect image classification within ductile cast iron pipe (DCIP) images. Leveraging the CutPaste-Mix data augmentation strategy, we combine defect-free data with enhanced data to input into a deep convolutional neural network. Through Gaussian Density Estimation, we compute anomaly scores to achieve the classification of abnormal regions. Our approach has been implemented in real-world scenarios, involving equipment installation, data collection, and experimentation. The results demonstrate the robust performance of our method, in both the DCIP image dataset and practical field application, achieving an impressive 99.5 AUC (Area Under Curve). This presents a cost-effective means of providing data support for subsequent DCIP surface inspection model training.

关键词： ductile cast iron pipe defect classification self-supervised CutPaste-Mix

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Welding defect classification Based on Lightweight CNN

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第11期37卷 2350026-2350026页

作者： Guo, Bo Wang, Youtao Li, Xu Zhou, Yeping Li, Jianmin Rao, Lanxiang Nanchang Inst Technol Nanchang Key Lab Welding Robot & Intelligent Tech Nanchang 330099 Jiangxi Peoples R China Jiangxi Gen Inst Testing & Certificat Nanchang 330052 Jiangxi Peoples R China Jiangxi Hengda Hi Tech Co Ltd Nanchang 330096 Jiangxi Peoples R China Jiangxi Sci & Technol Infrastruct Platform Ctr Nanchang 330003 Jiangxi Peoples R China

The welding defect classification method based on deep learning often faces problems such as insufficient training data and complex model structures, which affect its real-time performance. Therefore, a welding defect classification method based on lightweight convolutional neural network (CNN) is proposed. The problems of insufficient and unbalanced welding defect image are solved by using the generative adversarial networks (GANs) data augmentation method. A lightweight CNN model is developed, which reduces the structural parameters under the premise of ensuring classification accuracy. The feature data of each convolution layer are visualized to verify the feasibility of the model and improve the interpretability of the model. By comparing the accuracy and real-time performance with other lightweight models, the excellent performance of the proposed model in welding defect classification is verified. Additionally, our model achieves 98.25% accuracy on the MNIST dataset.

关键词： Welding defect lightweight CNN defect classification

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