Distributed-based Hierarchical Clustering System for Large-scale Semiconductor Wafers

Lee, Seung−Chul; Kim, Dae-Young

doi:10.1109/ieem.2018.8607492

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…They proposed a density-based clustering method that can be applied to the classification of mixed defect patterns. Other unsupervised learning approaches include K-means [8], hierarchical clustering [9], neural network clustering [10], dominant defective patterns finder [11], etc. In addition, semi-supervision [12] and active learning [13] have also been applied.…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…The precision, recall, F1-score, and hybrid matrices were selected to evaluate the model performance. The precision and recall can be expressed by formula (8) and formula (9), respectively. It can be determined that the two evaluation indicators are contradictory, and we need to comprehensively evaluate the performance of the model with the F1-score, as shown in formula (10).…”

Section: Performance Of Improved Densenetmentioning

confidence: 99%

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Wafer bin map inspection based on DenseNet

Qiao

Wang

et al. 2021

J. Cent. South Univ.

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Wafer bin map (WBM) inspection is a critical approach for evaluating the semiconductor manufacturing process. An excellent inspection algorithm can improve the production efficiency and yield. This paper proposes a WBM defect pattern inspection strategy based on the DenseNet deep learning model, the structure and training loss function are improved according to the characteristics of the WBM. In addition, a constrained mean filtering algorithm is proposed to filter the noise grains. In model prediction, an entropy-based Monte Carlo dropout algorithm is employed to quantify the uncertainty of the model decision. The experimental results show that the recognition ability of the improved DenseNet is better than that of traditional algorithms in terms of typical WBM defect patterns. Analyzing the model uncertainty can not only effectively reduce the miss or false detection rate but also help to identify new patterns.

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Section: Unsupervised Learningmentioning

confidence: 99%

Section: Performance Of Improved Densenetmentioning

confidence: 99%

Wafer bin map inspection based on DenseNet

Qiao

Wang

et al. 2021

J. Cent. South Univ.

View full text Add to dashboard Cite

show abstract

“…The second operation involves an agglomerative procedure over the previously refined clusters. In[68], they proposed a Distributed-based Hierarchical Clustering System for Large-Scale Semiconductor Wafers (DHCSSW) by applying the big data Spark framework to existing hierarchical clustering algorithm.…”

mentioning

confidence: 99%