Efficient FDC based on hierarchical tool condition monitoring scheme

Blue, Jakey; Roussy, Agnès; Thieullen, Alexis; Pinaton, Jacques

doi:10.1109/asmc.2012.6212927

Cited by 7 publications

(11 citation statements)

References 9 publications

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“…The FDC profiles are also synchronized by DDTW firstly. Following the hierarchical analysis in [7], we know there's a big and obvious abnormal pattern in the overall tool condition as shown in Fig. 5.…”

Section: Evaluation Of Case Studymentioning

confidence: 98%

“…To save the page in presenting the results of tool condition hierarchy, we simply put the overall tool condition and the gas group condition from here. For more details of the hierarchy, please refer to [7]. To further classify the root causes at the sensor level in gas group, the proposed R2R variation is calculated for the 13 SVIDs.…”

Section: Evaluation Of Case Studymentioning

confidence: 99%

“…The FDC data to be evaluated are collected from an ETCH tool, which are the same as the second case in [7]. Around 800 wafers of 31 SVIDs from one family of eight GATE recipes are analyzed.…”

Section: Evaluation Of Case Studymentioning

confidence: 99%

“…Following the concept of consolidating multiple FDC SVIDs into one single indicator based on GMV (Generalized Moving Variance) to monitor the tool health [6], Blue et al [7][8] propose a hierarchical tool condition monitoring scheme to more efficiently detect and diagnose tool faults. Fig.…”

Section: Introductionmentioning

confidence: 99%

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FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

Blue

Roussy

Pinaton

2014

25th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2014)

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Tool behavior modeling and diagnosis is a big challenge in modern semiconductor fabrication, in particular for the foundry and analog companies with high product-mix and complicated technology nodes. Tool condition monitoring has been practiced by implementing the FDC (Fault Detection and Classification) system and analyzing large amount of real-time equipment data. This paper continues the work of tool condition hierarchy, where the excursions can be detected in one single overall tool indicator and are intuitively drilled down to the level of sensor groups. A R2R (Run-to-Run) variation monitoring technique is developed in order to correlate the tool faults with single sensor and thus completes the diagnostic gap of the hierarchy. The tool condition monitoring then becomes efficient and tool fault diagnosis can be systematically topdown.

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Section: Evaluation Of Case Studymentioning

confidence: 98%

Section: Evaluation Of Case Studymentioning

confidence: 99%

Section: Evaluation Of Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

Blue

Roussy

Pinaton

2014

25th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2014)

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“…• (3) crack the data-segments for correlations using an arsenal of statistical techniques [1][2][3][4]; and…”

Section: Introductionmentioning

confidence: 99%

Case of small-data analysis for ion implanters in the era of big-data FDC

Hui

Mou

2013

ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference

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This paper presents a case study of constructing process models based on physical mechanisms of semiconductor manufacturing tools in attempts to predict behaviours of process conditions. Actual measurements from the processing tools are always corrupted with noises and crunching huge volumes of temporal traces of status variables very often fail to pinpoint the accurate fault conditions, not to mention any of their efficient classifications, should abnormal conditions really exist. The current fashion of moving into massive big data computing is yet to distill concrete correlations among tool conditions and impacts on process results of semiconductor devices. As an alternative before the foolproof maturity of big data cracking, and in contrast to the conventional black-box approach of statistical regressions, we take a fundamental view in constructing physical model of the ion implantation process for a flywheel implanter, first to calculate the motion trajectories and subsequently, the implantation dosage on the wafer. We summarize the underlying solution techniques in principles and leave the specific details of parameter calibrations to individual field practitioners. Keywords -advanced process control, fault detection and classification, model-based controlI. 978-1-4673-5007-5/13/$31.00

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A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

et al. 2021

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Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset by first, extracting and selecting the most significant statistical time-domain features. The selected features were then grouped into distinct clusters allowing for reduced computational complexity through a comparative study of four different and frequently used categories of unsupervised clustering algorithms in fault classification. The Synthetic Minority Over Sampling Technique (SMOTE) was then employed to balance the classes of the training samples from the various clusters which then served as inputs for training the supervised GWO-MLP. To validate the proposed hybrid technique (CLUST-SMOTE-GWO-MLP), it was compared with its distinct modifications (variants). The superiority of CLUST-SMOTE-GWO-MLP is demonstrated by outperforming all the distinct modifications in terms of test accuracy and seven other statistical performance evaluation metrics (error rate, sensitivity, specificity, precision, F score, Mathews Correlation Coefficient and geometric mean). The overall analysis indicates that the proposed CLUST-SMOTE-GWO-MLP is efficient and can be used to classify multiclass and imbalanced fault conditions. Article Highlights The issue of multiclass and imbalanced class outputs is addressed for improving predictive maintenance. A multiclass fault classifier based on clustering and optimised multi-layer perceptron with grey wolf is proposed. The robustness and feasibility of the proposed technique is validated on a complex hydraulic system dataset.

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Efficient FDC based on hierarchical tool condition monitoring scheme

Cited by 7 publications

References 9 publications

FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

Case of small-data analysis for ion implanters in the era of big-data FDC

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

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