WDP-BNN: Efficient wafer defect pattern classification via binarized neural network

Zhang, Qing; Zhang, Yuhang; Li, Jizuo; Li, Yongfu

doi:10.1016/j.vlsi.2022.04.003

Cited by 11 publications

(2 citation statements)

References 38 publications

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Section: Related Literaturementioning

confidence: 99%

“…Shen and Lee 17 proposed the wafer bin map recognition (WBMR) system embedded with three modules: data preprocessing, region classification, and systematic pattern recognition. Zhang et al 83 proposed a binarized neural network (BNN) to classify the wafer defect patterns. To overcome the imbalance problem and performance loss due to BNN, they also proposed data augmentation methods and random under-sampling method.…”

Section: Semiconductor Wafer Bin Map (Wbm) Analysismentioning

confidence: 99%

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A novel hybrid resampling for semiconductor wafer defect bin classification

Park

Pan

Montgomery

2022

Quality & Reliability Eng

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Generally, defective dies on semiconductor wafer maps tend to form spatial clusters in distinguishable patterns which contain crucial information on specific problems of equipment or process, thus it is highly important to identify and classify diverse defect patterns accurately. However, in practice, there exists a serious class imbalance problem, that is, the number of the defective dies on semiconductor wafer maps is usually much smaller than that of the non-defective dies. In various machine learning applications, a typical classification algorithm is, however, developed under the assumption that the number of instances for each class is nearly balanced. If the conventional classification algorithm is applied to a class imbalanced dataset, it may lead to incorrect classification results and degrade the reliability of the classification algorithm. In this research, we consider the semiconductor wafer defect bin data combined with wafer warpage information and propose a new hybrid resampling algorithm to improve performance of classifiers. From the experimental analysis, we show that the proposed algorithm provides better classification performance compared to other data preprocessing methods regardless of classification models.

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Section: Related Literaturementioning

confidence: 99%

Section: Semiconductor Wafer Bin Map (Wbm) Analysismentioning

confidence: 99%

A novel hybrid resampling for semiconductor wafer defect bin classification

Park

Pan

Montgomery

2022

Quality & Reliability Eng

View full text Add to dashboard Cite

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A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

Huang,

Meng,

Huang

2023

Cluster Comput

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Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps

Altantawy,

Yakout

2024

J Intell Manuf

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Due to the complexity and dynamics of the semiconductor manufacturing processes, wafer bin maps (WBM) present various defect patterns caused by various process faults. The defect type detection on wafer maps provides information about the process and equipment in which the defect occurred. Recently, automatic inspection has played a vital role in meeting the high-throughput demand, especially with deep convolutional neural networks (DCNN) which shows promising efficiency. At the same time, the need for a large amount of labeled and balanced datasets limits the performance of such approaches. In addition, complex DCNN in recognition tasks can provide redundant features that cause overfitting and reduce interpretability. In this paper, a new hybrid deep model for wafer map fault detection to get over these challenges is proposed. Firstly, a new convolutional autoencoder (CAE) is employed as a synthetization model to fix the high imbalance problem of the dataset. Secondly, for efficient dimensionality reduction, an embedding procedure is applied to the synthesized maps to get sparse encoded wafer maps by reinforcing a sparsity regularization in an encoder-decoder network to form a sparsity-boosted autoencoder (SBAE). The sparse embedding of wafer maps guarantees more discriminative features with 50% reduction in spatial size compared to the original wafer maps. Then, the 2D encoded sparse maps are converted to 1D sinograms to be fed later into another aggressive feature reduction stage using a new modified red deer algorithm with a new tinkering strategy. The resultant feature pool is reduced to ~ 25 1D feature bases, i.e., ~ 1.5% of the initial size of the 2D wafer maps. Finally, for the prediction stage, a simple 1DCNN model is introduced. The proposed inspection model is tested via different experiments on real-world wafer map dataset (WM-811K). Compared to state-of-the-art techniques, the proposed model outperforms their performance even with small-sized 1D feature pool. The average testing accuracy are 98.77% and 98.8% for 9 and 8 types of faults, respectively.

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WDP-BNN: Efficient wafer defect pattern classification via binarized neural network

Cited by 11 publications

References 38 publications

A novel hybrid resampling for semiconductor wafer defect bin classification

A novel hybrid resampling for semiconductor wafer defect bin classification

A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps

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