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Application of PARAFAC2 to fault detection and diagnosis in semiconductor etch

JOURNAL OF CHEMOMETRICS 2001年第4期15卷 285-298页

作者： Wise, BM Gallagher, NB Martin, EB Eigenvector Res Inc Manson WA 98831 USA Newcastle Univ Ctr Proc Analyt & Control Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Monitoring and fault detection of batch chemical processes are complicated by stretching of the time axis, resulting in batches of different length. This paper offers an approach to the unequal time axis problem using the parallel factor analysis 2 (PARAFAC2) model. Unlike PARAFAC, the PARAFAC2 model does not assume parallel proportional profiles, but only that the matrix of profiles preserves its 'inner product structure' from sample to sample. PARAFAC2 also allows each matrix in the multiway array to have a different number of rows. It has previously been demonstrated how the PARAFAC2 model can be used to model chromatographic data with retention time shifts. Fault detection and, to a lesser extent, diagnosis in a semiconductor etch process are considered in this paper. It is demonstrated that PARAFAC2 can effectively model batch process data from semiconductor manufacture with unequal dimension in one of the orders, such as the unequal batch length problem. It is shown that the PARAFAC2 model has approximately the same sensitivity to faults as other competing methods. including principal component analysis (PCA), unfold PCA (often referred to as multiway PCA), trilinear decomposition (TLD) and conventional PARAFAC. The advantage of PARAFAC2 is that it is easier to apply than MPCA, TLD and PARAFAC, because unequal batch lengths can be handled directly rather than through preprocessing methods. It also provides additional diagnostic information: the recovered batch profiles. It is likely, however, that it is less sensitive to faults than conventional PARAFAC. Copyright (C) 2001 John Wiley & Sons, Ltd.

关键词： multiway curve resolution multivariate statistical process control batch process monitoring

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multivariate monitoring of batch process startup

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2001年第4期17卷 269-278页

作者： Wurl, RC Albin, SL Shiffer, IJ Oregon State Univ Dept Ind & Mfg Engn Corvallis OR 97331 USA Rutgers State Univ Dept Ind Engn Piscataway NJ 08854 USA Orthoclin Diagnost Raritan NJ 08869 USA

A batch process is finite in duration and can be separated into two stages: startup and production. We develop a methodology to monitor a batch process during the startup stage to reduce the length of the startup stage. We focus on processes that are characterized by multiple process parameters and product characteristics. Because of the complex interdependencies characterizing the process parameters and product characteristics, it is more effective to evaluate them simultaneously. To address the multivariate nature of the process we use a multivariate statistical model: PLS (Projection to Latent Structures). PLS has been applied to several applications in statistical process monitoring. We present a new application of PLS to the startup stage of a batch process. Iterative adjustments made during startup in search of an acceptable production zone consume considerable amounts of material, labor and equipment time. We develop a monitoring procedure to reduce the time as well as the number of iterations and adjustments needed for startup. A PLS model is constructed, using baseline data, to characterize the relationship among process parameters during good production. The startup stage is monitored using the PLS characterization to determine if the process is consistent with good production. We illustrate the improved startup operations with an example from a batch process in filament extrusion, the application that motivates this work. Copyright (C) 2001 John Wiley & Sons, Ltd.

关键词： batch process startup statistical process monitoring multivariate statistical process control projection to latent structures (PLS)

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multivariate statistical monitoring of a high-pressure polymerization process

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POLYMER REACTION ENGINEERING 2003年第4期11卷 1017-1052页

作者： Kumar, V Sundararaj, U Shah, SL Hair, D Vande Griend, LJ Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada AT Plast Inc Edmonton AB Canada

The high pressure LDPE (low density polyethylene) industrial process operates under supercritical conditions, and so it is necessary to monitor its performance to prevent abnormal situations. Extreme deviations from the normal operating region lead to conditions such as: loss of normal reaction, decompositions of the reactants, and lost production due to outages. multivariate statistical process control strategies operate on top of the DCS (distributed control system) to detect and diagnose abnormal process behavior and provide the operators an opportunity to take preventative operational actions. process engineers may also use it for off-line diagnosis of poorly understood processes. In this work, data from a commercial LDPE/EVA (ethylene-vinyl acetate copolymer) high-pressure unit using an OPC (Object linking and embedding for process control) server installed on the DCS is used to build empirical models and perform fault detection. Data transfer issues, preprocessing, process model development using Principal Components Analysis (PCA) and first principles modelling of critical equipment are provided. In addition to showing grade transitions in the latent variable space, the models were used to detect process shifts. process data from various real faults were considered and it was established that PCA could be employed to predict and diagnose process faults. The study gives recommendations for process monitoring strategies.

关键词： high pressure process low density polyethylene LDPE industrial evaluation multivariate statistical process control principal components analysis PCA process monitoring fault detection and diagnosis

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Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing processes

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2011年第3期24卷 432-444页

作者： Yu, Jianbo Shanghai Univ Dept Mech Automat Engn Shanghai 200072 Peoples R China

Fault detection has been recognized in the semiconductor industry as an effective component of advanced process control framework in increasing yield and product quality. Recently, conventional process monitoring-based principal component analysis (PCA) has been applied to semiconductor manufacturing by quickly detecting when the process abnormalities have occurred. However, the unique characteristics of the semiconductor processes, nonlinearity in most batch processes, multimodal batch trajectories due to multiple operating conditions, significantly limit the applicability of PCA to the fault detection of semiconductor manufacturing. To explicitly address these unique issues in semiconductor processes, a principal components (PCs)-based Gaussian mixture model (GMM) (named PCGMM) has been proposed in this paper. GMM is capable to handle complicated data with nonlinearity or multimodal features by a mixture of multiple Gaussian components, which is very suitable to describe observations from semiconductor processes. Furthermore, two quantification indexes (negative Log likelihood probability and Mahalanobis distance) are proposed for assessing process states, and a Bayesian inference-based calculation method is further used to provide the process failure probability. The validity and effectiveness of PCGMM-based fault detection model are illustrated through two simulation processes and a semiconductor manufacturing process. The experimental results demonstrated that the proposed model is superior to PCA-based monitoring models and can achieve accurate and early detection of various types of faults in complicated manufacturing processes.

关键词： Fault detection Gaussian mixture model multivariate statistical process control principal component analysis semiconductor manufacturing process

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Integrating independent component analysis and local outlier factor for plant-wide process monitoring

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JOURNAL OF process control 2011年第7期21卷 1011-1021页

作者： Lee, Jaeshin Kang, Bokyoung Kang, Suk-Ho Seoul Natl Univ Dept Ind Engn Seoul 151742 South Korea

We propose a novel process monitoring method integrating independent component analysis (ICA) and local outlier factor (LOF). LOF is a recently developed outlier detection technique which is a density-based outlierness calculation method. In the proposed monitoring scheme, ICA transformation is performed and the control limit of LOF value is obtained based on the normal operating condition (NOC) dataset. Then, at the monitoring phase, the LOF value of current observation is computed at each monitoring time, which determines whether the current process is a fault or not. The comparison experiments are conducted with existing ICA-based monitoring schemes on widely used benchmark processes, a simple multivariate process and the Tennessee Eastman process. The proposed scheme shows the improved accuracy over existing schemes. By adopting LOF, the monitoring statistic is computed regardless of data distribution. Therefore, the proposed scheme integrating ICA and LOF is more suitable for real industry where the monitoring variables are the mixture of Gaussian and non-Gaussian variables, whereas existing ICA-based schemes assume only non-Gaussian distribution. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Local outlier factor Independent component analysis multivariate statistical process control Fault detection process monitoring Tennessee Eastman process

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A distribution-free method for process monitoring

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9821-9829页

作者： Ge, Zhiqiang Song, Zhihuan Zhejiang Univ State Key Lab Ind Control Technol Inst Ind Proc Control Hangzhou 310027 Zhejiang Peoples R China

Traditional multivariate statistical process control methods such as principal component analysis are limited to Gaussian process data when they used for process monitoring. However, the deficiency is not due to the method itself, but lies in the monitoring statistic construction and its confidence limit determination. This paper proposed a distribution-free method, which employs the one-class SVM to construct new monitoring statistics. Thus two new statistics are developed separately in two subspaces of the PCA model: the principal component subspace and the residual subspace. When some fault has been detected, a novel fault reconstruction scheme is proposed. For fault identification, two new identification indices are constructed. The performance of the proposed method in fault detection, reconstruction and identification is evaluated through a case study of the Tennessee Eastman (TE) benchmark process. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fault detection Fault reconstruction Fault identification One-class SVM multivariate statistical process control

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Model selection for partial least squares regression

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2002年第1期64卷 79-89页

作者： Li, BB Morris, J Martin, EB Univ Newcastle Upon Tyne Ctr Proc Analyt & Control Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Partial least squares (PLS) regression is a powerful and frequently applied technique in multivariate statistical process control when the process variables are highly correlated. Selection of the number of latent variables to build a representative model is an important issue. A metric frequently used by chemometricians for the determination of the number of latent variables is that of Wold's R criterion, whilst more recently a number of statisticians have advocated the use of Akaike Information Criterion (AIC). In this paper, a comparison between Wold's R criterion and AIC for the selection of the number of latent variables to include in a PLS model that will form the basis of a multivariate statistical process control representation is undertaken based on a simulation study. It is shown that neither Wold's R criterion nor AIC exhibit satisfactory performance. This is in contrast to the adjusted Wold's R criteria which is shown to demonstrate satisfactory performance in terms of the number of times the known true model is selected. Two industrial applications are then used to demonstrate the methodology. The first relates to the modelling of a product quality using data from an industrial fluidised bed reactor and the second focuses on an industrial NIR data set, The results are consistent with those of the simulation studies. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： Akaike Information Criterion (AIC) cross-validation model selection multivariate statistical process control partial least squares regression Wold's R criterion

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Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study

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TALANTA 2018年 179卷 292-299页

作者： Catelani, Tiago A. Santos, Joao Rodrigo Pascoa, Ricardo N. M. J. Pezza, Leonardo Pezza, Helena R. Lopes, Joao A. Univ Estadual Paulista Inst Quim UNESP R Prof Francisco Degni 55POB 355 BR-14800900 Araraquara SP Brazil Univ Porto LAQV REQUIMTE Dept Quim & Bioquim Fac Ciencias Oporto Portugal Univ Porto LAQV REQUIMTE Lab Quim Aplicada Dept Ciencias QuimFac Farm Oporto Portugal Univ Lisbon Fac Farm Res Inst Med iMed ULisboa Lisbon Portugal

This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T-2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.

关键词： Coffee roasting Real-time monitoring multivariate statistical process control Near-infrared spectroscopy Principal component analysis

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High-Dimensional process Monitoring and Fault Isolation via Variable Selection

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JOURNAL OF QUALITY TECHNOLOGY 2009年第3期41卷 247-258页

作者： Wang, Kaibo Jiang, Wei Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Kowloon Hong Kong Peoples R China

Both process monitoring and fault isolation are important and challenging tasks for quality control and improvement in high-dimensional processes. Under a practical assumption that not all variables would shift simultaneously, this paper proposes a variable-selection-based multivariate statistical process control (SPC) procedure for process monitoring and fault diagnosis. A forward-selection algorithm is first utilized to screen out potential out-of-control variables;a multivariate control chart is then set up to monitor suspicious variables. Therefore, detection of faulty conditions and isolation of faulty variables can be achieved in one step. Both simulation studies and a real example have shown the effectiveness of the proposed procedure.

关键词： Forward Selection Linear Regression multivariate statistical process control T-2 Chart

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A one-sided MEWMA chart for health surveillance

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2008年第5期24卷 503-518页

作者： Joner, Michael D., Jr. Woodall, William H. Reynolds, Marion R., Jr. Fricker, Ronald D., Jr. Procter & Gamble Co Mason OH 45040 USA Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Dept Forestry Blacksburg VA 24061 USA USN Postgrad Sch Dept Operat Res Monterey CA 93943 USA

It is often important to rapidly detect all increase in the incidence rate of a given disease or other medical condition. It has been shown that when disease counts are sequentially available from a single region, a univariate control chart designed to detect rate increases, such as a one-sided cumulative sum chart, is very affective. When disease counts are available from several regions at corresponding times, the most efficient monitoring method is not readily apparent. multivariate monitoring methods have been suggested for dealing with this detection problem. Some of these approaches have shortcomings that have been recently demonstrated in the quality control literature. We discuss these limitations and suggest an alternative multivariate exponentially weighted moving average chart. We compare the average run-length performance of this chart with that of competing methods. We also evaluate the statistical performance of these charts when the actual increase ill the disease count rate is different from the one that the chart was optimized to detect quickly. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： disease surveillance one-sided control charts monitoring multivariate statistical process control spatial correlation

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