Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis

Paynabar, Kamran; Jin, Jesse S.; Pacella, Massimo

doi:10.1080/0740817x.2013.770187

Cited by 80 publications

(41 citation statements)

References 30 publications

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“…This The tonnage profile dataset was used in previous research by Lei et al (2010) andPaynabar et al (2013). However, as discussed earlier, both of these studies used a subset of labeled profiles to train, and another subset to test their models.…”

Section: Case Study: Phase-i Monitoring and Diagnosis Of Multichannelmentioning

confidence: 98%

“…There are several issues in using VFPCA for multichannel profiles; see Paynabar et al (2013) for related discussions. In particular, under our models (1) and (2), this approach breaks the correlation structure in the original data, and potentially loses the useful representations that can be obtained in the original form.…”

Section: Simulation Studymentioning

confidence: 99%

“…Alternatively, one may analyze each profile channel separately. This approach, however, overlooks the inter-relationship among profile channels that provides valuable information about the process (See for example Paynabar et al, 2013). In order to fully utilize the information of multichannel profiles, Paynabar et al (2013) used multilinear dimension reduction techniques to extract informative features from multichannel profiles, while considering their inter-relationships.…”

Section: Introductionmentioning

confidence: 99%

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A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

2016

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Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multi-operation forging processes, we propose a new modeling, monitoring and diagnosis framework for phase-I analysis of multichannel profiles. The proposed framework is developed under the assumption that different profile channels have similar structure so that we can gain strength by borrowing information from all channels. The multi-dimensional functional principal component analysis is incorporated into change-point models to construct monitoring statistics. Simulation results show that the proposed approach has good performance in identifying change-points in various situations compared with some existing methods. The codes for implementing the proposed procedure are available in the supplementary material.

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Section: Case Study: Phase-i Monitoring and Diagnosis Of Multichannelmentioning

confidence: 98%

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

2016

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“…Montgomery and Mastrangelo, 41 Psarakis and Papal, 42 Staudlammer et al, 31 Jensen et al, 43 Noorossana et al, 44 Soleimani et al, 45 Qiu et al, 46 Guo et al, 47 Amiri and Zou, 38 and Zhang et al 48 are some typical examples. Extensive studies have been conducted on nonlinear profiles, some of which could be found in Jin and Shi, 7,49 Lada et al, 50 Ding et al, 51 Jeong et al, 52 Zhou et al, 53 Zhang and Albin, 54 Chicken et al, 55 Chang and Yadama, 56 Paynabar and Jin, 57 Guo et al, 47 Paynabar et al, 58 Fan et al, 59 Nikoo and Noorossana, 60 and McGinnity et al 61 It is good to mention several other studies using other approaches, which are listed as follows: Zeng and Chen 62 used Bayesian hierarchical approach. Zou et al 63 used penalized regression model to detect outliers.…”

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confidence: 98%

An Area‐based Methodology for the Monitoring of General Linear Profiles

Motasemi

Alaeddini

Zou

2016

Quality & Reliability Eng

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Statistical profile monitoring has received much attention during recent years. While numerous contributions and applications have been demonstrated in the literature, the control statistics across many of the proposed methodologies have mainly remained unchanged, which somehow hinders further improvement of the monitoring schemes. In this paper, however, we propose a novel approach to leverage the information in the area formed between the sampled and incontrol profile to improve the monitoring scheme performance. Specifically, we develop a control statistic based on the convolution of the observed and in-control profiles to monitor the shifts in the slope and intercept parameters. We also extend the mean square statistic to area weighted total sum of squares, to more effectively monitor the shift in the standard deviation. Extensive simulation studies are conducted to demonstrate the performance of the proposed methodology in comparison with some of the existing approaches.

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“…Some other authors proposed statistical methods based on the introduction of wavelet analysis, which will be presented shortly. The simple nonlinear regression is also studied by Chang and Yadama (2010), Paynabar et al (2013).…”

Section: Nonlinear Profilesmentioning

confidence: 99%

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

Zakour¹,

Taleb

2017

J Ind Eng Int

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Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small (0.1%). It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy (OES) sensor is employed. The huge number of gathered wavelengths (profiles) is then analyzed and pre-processed using a new proposed simple algorithm named Spectra peak selection (SPS) to select the important wavelengths, then we employ wavelet analysis (WA) to enhance the performance of detection by suppressing noise and redundant information. The selected and treated OES wavelengths are then used in modified multivariate control charts (MEWMA and Hotelling) for three statistics (mean, SD and CV) and windowed polynomial regression for mean. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic. As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon.

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Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis

Cited by 80 publications

References 30 publications

A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

A Change-Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

An Area‐based Methodology for the Monitoring of General Linear Profiles

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

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