A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter

Lin, Shin-Yeu; Horng, Shih-Cheng

doi:10.1109/tsm.2006.883594

Cited by 16 publications

(7 citation statements)

References 28 publications

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“…Ion Implantation is used to modify the electrical properties of wafers by injecting doping atoms. It is often considered a 'bottleneck' in production lines due to the high cost of the tool, making it a critical operation for throughput [11].…”

Section: A Use Casementioning

confidence: 99%

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Susto

Schirru

Pampuri

et al. 2015

IEEE Trans. Ind. Inf.

643

223

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In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called 'health factors' or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

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Section: A Use Casementioning

confidence: 99%

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Susto

Schirru

Pampuri

et al. 2015

IEEE Trans. Ind. Inf.

643

223

View full text Add to dashboard Cite

show abstract

“…Regarding other machines, Lin and Horng [111] use a scheme of classification and detection of faults in an ion implanter, proposing a hybrid classification tree, i.e., they combine a grouping algorithm with CART. They indicate that their methodology is general and can be applied to other machines by simply modifying the warning generation criteria.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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“…During the process, the filament is heated and electrons are 'boiled' off the heated filament; the electrons are then accelerated in the beamline area and then impinge on the wafers in the End Station area. Ion Implantation is used to modify the electrical properties of the wafers by injecting doping atoms and it is consider a 'bottleneck' in the production line due to the high cost of the tool, making it a critical operation for throughput [10].…”

Section: Use Case and Data Descriptionmentioning

confidence: 99%

A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation

Susto

Schirru

Pampuri

et al. 2013

2013 IEEE International Conference on Automation Science and Engineering (CASE)

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In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset.

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A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter

Cited by 16 publications

References 28 publications

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation

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