Mixup-based classification of mixed-type defect patterns in wafer bin maps

Shin, Wooksoo; Kahng, Hyungu; Kim, Seoung Bum

doi:10.1016/j.cie.2022.107996

Cited by 24 publications

(5 citation statements)

References 17 publications

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“…The robustness of the proposed method is validated by its superior performance on publicly available datasets. German TILDA defect database 27 , 40 , 41 ( https://www.aitex.es/afid/ TILDA-C1R1 41 , TILDA-C2R2 41 , TILDA-C2R3 41 ), MT 42 ( https://github.com/abin24/Magnetic-tile-defect-datasets ), and AITEX 43 ( https://www.aitex.es/afid/ ) datasets are employed to evaluate the model. The classification performance in terms of accuracy of these datasets is shown in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

A voting-based ensemble feature network for semiconductor wafer defect classification

Misra

Kim

et al. 2022

Sci Rep

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Semiconductor wafer defects severely affect product development. In order to reduce the occurrence of defects, it is necessary to identify why they occur, and it can be inferred by analyzing the patterns of defects. Automatic defect classification (ADC) is used to analyze large amounts of samples. ADC can reduce human resource requirements for defect inspection and improve inspection quality. Although several ADC systems have been developed to identify and classify wafer surfaces, the conventional ML-based ADC methods use numerous image recognition features for defect classification and tend to be costly, inefficient, and time-consuming. Here, an ADC technique based on a deep ensemble feature framework (DEFF) is proposed that classifies different kinds of wafer surface damage automatically. DEFF has an ensemble feature network and the final decision network layer. The feature network learns features using multiple pre-trained convolutional neural network (CNN) models representing wafer defects and the ensemble features are computed by concatenating these features. The decision network layer decides the classification labels using the ensemble features. The classification performance is further enhanced by using a voting-based ensemble learning strategy in combination with the deep ensemble features. We show the efficacy of the proposed strategy using the real-world data from SK Hynix.

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Section: Resultsmentioning

confidence: 99%

A voting-based ensemble feature network for semiconductor wafer defect classification

Misra

Kim

et al. 2022

Sci Rep

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“…Takeshi et al [16] generated synthetic WBM images for training, achieving high-accuracy classification. Shin et al [24] used MixUp augmentation in combination with a CNN for identifying defective patterns.…”

Section: Fully Supervised Approachesmentioning

confidence: 99%

“…It enhances accuracy, reduces manual inspection time and costs, and boosts operational efficiency. Although numerous deep learning-based methods (e.g., [1,2,4,5,17,22,24]) have been proposed for automating WBM pattern classification, they often require substantial labeled data, which is costly and time-consuming to obtain. To address this issue, Active Learning-based approaches were introduced for WBM pattern classification.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-labeling and clustering-based active learning for imbalanced classification of wafer bin map defects

Manivannan

2023

SIViP

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“…Zhuang et al [41] employed a real and private dataset of six individual types to train an ensemble of deep belief networks. Shin et al produced mixedtype wafer maps through data augmentation, combining WM-811K single defect wafer maps [42]. These studies are based on limited data collected from own sources or generated synthetically, unavailable in the public domain for model benchmarking.…”

Section: A Deep Learning For Mixed-type Wafer Map Defect Classificationmentioning

confidence: 99%

An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification

Batool,

Shapiai,

Mostafa

et al. 2023

IEEE Access

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Silicon wafer defect classification is crucial in improving fabrication and chip production. While deep learning methods have been successful in single-defect wafer classification, the increasing complexity of the fabrication process has introduced the challenge of multiple defects on wafers, which requires more robust feature learning and classification techniques. Attention mechanisms have been used to enhance feature learning for multiple wafer defects. However, they have limited use in a few mixed-type defect categories, and their performance declines as the number of mixed patterns increases. This work proposes an attention-augmented convolutional neural networks (A2CNN) model for enhanced discriminative feature learning of complex defects. The A2CNN model emphasizes the features in the channel and spatial dimensions. Additionally, the model adopts the focal loss function to reduce misclassification and a global average pooling layer to enhance the network's generalization by reducing overfitting. The A2CNN model is evaluated on the MixedWM38 wafer defect dataset using 10-fold cross-validation. It achieves impressive results, with accuracy, precision, recall, and F1-score reported as 98.66%, 99.0%, 98.55%, and 98.82% respectively. Compared to existing works, the A2CNN model performs better by effectively learning valuable information for complex mixed-type wafer defects.

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Mixup-based classification of mixed-type defect patterns in wafer bin maps

Cited by 24 publications

References 17 publications

A voting-based ensemble feature network for semiconductor wafer defect classification

A voting-based ensemble feature network for semiconductor wafer defect classification

Pseudo-labeling and clustering-based active learning for imbalanced classification of wafer bin map defects

An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification

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