A machine-learning classification approach for IC manufacturing control based on test structure measurements

Zaghloul, Mona; Khera, D.; Linholm, L.W.; Reeve, Charles P.

doi:10.1109/66.24928

Cited by 16 publications

(3 citation statements)

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“…While the use of machine learning in production research dates to 1980s (e.g. Lu and Ham 1989;Shaw and Whinston 1989;Zaghloul et al 1989), it has begun to gain acceptance in recent years. Machine learning algorithms can process symbolic data (e.g.…”

Section: Machine Learning In Production Systemsmentioning

confidence: 99%

From data to big data in production research: the past and future trends

Kuo

Kusiak

2018

International Journal of Production Research

153

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Data have been utilised in production research in meaningful ways for decades. Recent years have offered data in larger volumes and improved quality collected from diverse sources. The state-of-the-art data research in production and the emerging methodologies are discussed. The review of the literature suggests that production research enabled by data has shifted from that based on analytical models to data-driven. Manufacturing and data envelopment analysis have been the most popular application areas of data-driven methodologies. The research published to date indicates that data mining is becoming a dominant methodology in production research. Future trends and opportunities for data-driven production research are presented.

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Section: Machine Learning In Production Systemsmentioning

confidence: 99%

From data to big data in production research: the past and future trends

Kuo

Kusiak

2018

International Journal of Production Research

153

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“…In this category, one may classify the following systems: decision tree induction [29], learning with genetic algorithms [81] similruity-based learning [145,139], CLUSTERJ2 [139], ID3 [175] and the classification approach for IC manufacturing control [229]. In this category, one may classify the following systems: decision tree induction [29], learning with genetic algorithms [81] similruity-based learning [145,139], CLUSTERJ2 [139], ID3 [175] and the classification approach for IC manufacturing control [229].…”

Section: In Learning From Examples a Set Of Examples And Counter Examentioning

confidence: 99%

Artificial Intelligence in Industrial Decision Making, Control and Automation

Tzafestas¹,

Verbruggen²

1995

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“…Machine learning classification models find various applications in manufacturing industries. Zaghloul et al [5] used machine learning classification approach for classifying electrical measurement results from a custom-designed test chip. Kim et al [6] trained the models with Fault Detection and Classification (FDC) data for detection of the faulty wafer in semiconductor manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint

Mishra¹

2020

Applied Engineering Letters

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Data analysis is divided into two categories i.e. classification and prediction. These two categories can be used for extraction of models from the dataset and further determine future data trends or important set of classes available in the dataset. The aim of the present work is to determine location of the fracture failure in dissimilar friction stir welded joint by using various machine learning classification models such as Decision Tree, Support Vector Machine (SVM), Random Forest, Naïve Bayes and Artificial Neural Network (ANN). It is observed that out of these classification algorithms, Artificial Neural Network results have the best accuracy score of 0.95.

show abstract

A machine-learning classification approach for IC manufacturing control based on test structure measurements

Cited by 16 publications

References 1 publication

From data to big data in production research: the past and future trends

From data to big data in production research: the past and future trends

Artificial Intelligence in Industrial Decision Making, Control and Automation

Machine Learning Classification Models for Detection of the Fracture Location in Dissimilar Friction Stir Welded Joint

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