A multivariate parameter trace analysis for online fault detection in a semiconductor etch tool

Ko, Jong Myoung; Kim, Chang Ouk

doi:10.1080/00207543.2011.611538

Cited by 11 publications

(1 citation statement)

References 20 publications

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“…In the manufacturing as well as injection molding domain, statistical approaches are used for process control and fault detection [53]. In particular, various transform techniques are used as a feature extraction method for multivariate timeseries data collected from multiple sensors [54]- [57]. After applying an adequate feature transformation, statistical data mining techniques are used for fault detection or classification [58].…”

Section: B Time-series Analysis In a Manufacturing Domainmentioning

confidence: 99%

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

Kim

Choi

et al. 2021

IEEE Access

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The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an endto-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem. INDEX TERMS Machine learning, deep learning, multimodal learning, early fusion, industrial AI, plastic injection moldingThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: B Time-series Analysis In a Manufacturing Domainmentioning

confidence: 99%

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

Kim

Choi

et al. 2021

IEEE Access

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Least angle sparse principal component analysis for ultrahigh dimensional data

Xie,

Wang,

Kim

et al. 2024

Ann Oper Res

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Principal component analysis (PCA) has been a widely used technique for dimension reduction while retaining essential information. However, the ordinary PCA lacks interpretability, especially when dealing with large scale data. To address this limitation, sparse PCA (SPCA) has emerged as an interpretable variant of ordinary PCA. However, the ordinary SPCA relies on solving a challenging non-convex discrete optimization problem, which maximizes explained variance while constraining the number of non-zero elements in each principal component. In this paper, we propose an innovative least angle SPCA technique to address the computational complexity associated with SPCA, particularly in ultrahigh dimensional data, by sequentially identifying sparse principal components with minimal angles to their corresponding components extracted through ordinary PCA. This sequential identification enables solving the optimization problem in polynomial time, significantly reducing computational challenges. Despite its efficiency gains, our proposed method also preserves the main attributes of SPCA. Through comprehensive experimental results, we demonstrate advantages of our approach as a viable alternative for dealing with the computational difficulties inherent in ordinary SPCA. Notably, our method emerges as an efficient and effective solution for conducting ultrahigh dimensional data analysis, enabling researchers to extract meaningful insights and streamline data interpretation.

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A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance

Kim

2021

J Intell Manuf

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A multivariate parameter trace analysis for online fault detection in a semiconductor etch tool

Cited by 11 publications

References 20 publications

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

Least angle sparse principal component analysis for ultrahigh dimensional data

A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance

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