Wafer Map Defect Pattern Recognition using Imbalanced Datasets

Tziolas, Theodoros; Θεοδοσίου, Θεοδόσιος; Παπαγεωργίου, Κ.; Rapti, Aikaterini; Dimitriou, Nikolaos; Tzovaras, Dimitrios; Papageorgiou, Elpiniki I.

doi:10.1109/iisa56318.2022.9904402

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Wang et al [13] classified defect patterns by implementing a general model using the MobileNet V2 algorithm and a lightweight model with 24.77% fewer parameters. T. Tziolas et al [18] proposed a methodology for classifying WM-811K by using different data processing techniques for each class to address the issue of data imbalance. Ebayyeh et al [19] proposed a data augmentation method for classifying WM-811K by using different data processing techniques for each class to address the issue of data imbalance.…”

Section: Discussionmentioning

confidence: 99%

“…Nakazawa et al and Shim et al balanced the data by using a small amount of real wafer data and additional synthesized data [5,17]. To address the imbalance in the dataset, Theodoros Tziolas et al [18] independently processed each class in proportion to the number of samples. Abu Ebayyeh et al [19] augmented the data in a balanced manner using a deep convolutional generative adversarial network (DCGAN) and then utilized a capsule network for classification.…”

Section: Single-failure Pattern Classificationmentioning

confidence: 99%

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Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

Kim,

Kim

2024

Electronics

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Defect pattern analysis of wafer bin maps (WBMs) is an important means of identifying process problems. Recently, automated analysis methods using machine learning or deep learning have been studied as alternatives to manual classification by engineers. In this paper, we propose a method to improve the feature extraction performance of defect patterns by transforming the polar coordinate system instead of the existing WBM image input. To reduce the variability of the location representation, defect patterns in the Cartesian coordinate system, where the location of the distributed defect die is not constant, were converted to a polar coordinate system. The CNN classifier, which uses polar coordinate transformed input, achieved a classification accuracy of 91.3%, which is 4.8% better than the existing WBM image-based CNN classifier. Additionally, a tree-structured classifier model that sequentially connects binary classifiers achieved a classification accuracy of 94%. The method proposed in this paper is also applicable to the defect pattern classification of WBMs consisting of different die sizes than the training data. Finally, the paper proposes an automated pattern classification method that uses individual classifiers to learn defect types and then applies ensemble techniques for multiple defect pattern classification. This method is expected to reduce labor, time, and cost and enable objective labeling instead of relying on subjective judgments of engineers.

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Section: Discussionmentioning

confidence: 99%

Section: Single-failure Pattern Classificationmentioning

confidence: 99%

Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

Kim,

Kim

2024

Electronics

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“…At present, the only public large data set related to wafer defect analysis is WM-811k, which contains 811,457 crystal circle distribution patterns. Almost all the research on wafer defect distribution is verified on this data set [15], [16]. However, this data set can only be used to study the location distribution of defects in the entire wafer map.…”

Section: Wafer Defect Data Setmentioning

confidence: 99%

Ra-Unet: A New Deep Learning Segmentation Method for Semiconductor Wafer Defect Analysis on Fine-Grained Scanning Electron Microscope (Sem) Images

Qiao,

Mei,

Luo

et al. 2024

Preprint

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“…It is worth mentioning that real industrial data are often scarce or imbalanced [7]. This poses a challenge in the development of robust solutions with deep learning, and hence transfer learning and data augmentation techniques have been employed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Efficientdet Application for Detection of Incorrect Assemblies in the Antenna Manufacturing Process

Feleki,

Papageorgiou,

Tziolas

et al. 2023

10th ECCOMAS Thematic Conference on Smart Structures and Materials

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This work aims to investigate and effectively apply the new deep learning methods for object detection from the family of EfficientDet-Lite algorithms for the detection of incorrect assemblies in the antenna manufacturing process. In the proposed methodology, stateof-the-art pre-trained models are exploited and fine-tuned in a real industrial dataset with limited samples. The defects considered in the dataset include metal cracks, plastic cracks, and housing imperfection. The proposed approach with EfficientDet-Lite2 can achieve an Average Precision (AP50) of 73.68%. A Yolov5-based model was also developed and extracted comparable results. Moreover, the Average Precision for each class was computed. For conducting the experiments, the TensorFlow Lite object detection API was employed. The results of this research work are promising and support further investigation of these types of deep learning for defect detection in manufacturing.

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Wafer Map Defect Pattern Recognition using Imbalanced Datasets

Cited by 6 publications

References 17 publications

Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

Ra-Unet: A New Deep Learning Segmentation Method for Semiconductor Wafer Defect Analysis on Fine-Grained Scanning Electron Microscope (Sem) Images

Efficientdet Application for Detection of Incorrect Assemblies in the Antenna Manufacturing Process

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