Equipment fault detection using spatial signatures

Gardner, Martha; Lu, Jye‐Chyi; Gyurcsik, R.S.; Wortman, J. J.; Hornung, B.; Heinisch, H.H.; Rying, E.A.; Rao, Suraj; Davis, Joseph G.; Mozumder, P.K.

doi:10.1109/3476.650961

Cited by 46 publications

(26 citation statements)

References 13 publications

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“…We denote this metric by M i4 . Other metrics are proposed in Gardner et al 32 , who noted that metrics can be defined to detect changes in profiles resulting from particular known process faults. One of these metrics is the sum of squared differences between each estimated profile and the average profile, denoted 2 .…”

Section: Non-parametric Approachmentioning

confidence: 98%

Statistical monitoring of nonlinear product and process quality profiles

Williams

Woodall

Birch

2007

Quality & Reliability Eng

244

206

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In many quality control applications, use of a single (or several distinct) quality characteristic(s) is insufficient to characterize the quality of a produced item. In an increasing number of cases, a response curve (profile) is required. Such profiles can frequently be modeled using linear or nonlinear regression models. In recent research others have developed multivariate T 2 control charts and other methods for monitoring the coefficients in a simple linear regression model of a profile. However, little work has been done to address the monitoring of profiles that can be represented by a parametric nonlinear regression model. Here we extend the use of the T 2 control chart to monitor the coefficients resulting from a parametric nonlinear regression model fit to profile data. We give three general approaches to the formulation of the T 2 statistics and determination of the associated upper control limits for Phase I applications. We also consider the use of non-parametric regression methods and the use of metrics to measure deviations from a baseline profile. These approaches are illustrated using the vertical board density profile data presented in Walker and Wright (Comparing curves using additive models.

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Section: Non-parametric Approachmentioning

confidence: 98%

Statistical monitoring of nonlinear product and process quality profiles

Williams

Woodall

Birch

2007

Quality & Reliability Eng

244

206

View full text Add to dashboard Cite

show abstract

“…Comparatively, a non-parametric method (Raja, Muralikrishnan, and Fu 2002) may have better performance. Some popular models, such as spline (Gardner et al 1997;Wang 1998;Tait et al 2006), and kernel method (Qiu, Zou, and Wang 2010) have been seen used for fitting 1-dimensional profiles. Such non-parametric models are usually less constrained and have strong robustness and adaptability.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A hierarchical model for characterising spatial wafer variations

Bao

Wang

Jin

2013

International Journal of Production Research

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Silicon wafers are commonly used materials in the semiconductor manufacturing industry. Their geometric quality directly affects the production cost and yield. Therefore, improvement in the quality of wafers is critical for meeting the current competitive market needs. Conventional summary metrics such as total thickness variation, bow and warp can neither fully reflect the local variability within each wafer nor provide useful insight for root cause diagnosis and quality improvement. The advancement of sensing technology enables two-dimensional (2D) data mapping to characterise the geometric shapes of wafers, which provides more information than summary metrics. The objective of this research is to develop a statistical model to characterise the thickness variation of wafers based on 2D data maps. Specifically, the thickness variation of wafers is decomposed into macro-scale and micro-scale variations, which are modelled as a cubic curve and a first-order intrinsic Gaussian Markov random field, respectively. The models can successfully capture both the macro-scale mean trend and the micro-scale local variation, with important engineering implications for process monitoring, fault diagnosis and run-to-run control. A practical case study from a wafer manufacturing process is performed to show the effectiveness of the proposed methodology.

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“…The proposed method provides a means to model and analyse a large number of profiles simultaneously by projecting them into functional space. Profile data are not only popular in business applications, but are also frequently collected in manufacturing processes (Runger et al 1996, Gardner et al 1997. For example, linear models have been widely investigated in manufacturing applications for quality improvement (Woodall et al 2004).…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Churn detection via customer profile modelling

Qian

Jiang

Tsui

2006

International Journal of Production Research

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Customer profile modelling is an essential element of marketing in service applications to aid understanding customer behaviour, designing customized service plans, and preventing churn activities. In many service applications, profile data are often generated that consist of customer transactions over time. This paper proposes using a functional mixture model to profile customer behaviour in order to identify and capture churn activity patterns. A five-step procedure is proposed based on the functional mixture model, which includes: (1) standardizing profiles, (2) screening out uninteresting profiles, (3) projecting profiles onto a feature space represented by a set of basis functions, (4) applying clustering algorithms to the resultant coefficients in the feature space, and (5) identifying interesting profiles. It is shown that the proposed framework is effective for detecting churn activities in the telecommunication industry. The method can also be easily generalized to model sophisticated manufacturing processes for quality improvement.

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Equipment fault detection using spatial signatures

Cited by 46 publications

References 13 publications

Statistical monitoring of nonlinear product and process quality profiles

Statistical monitoring of nonlinear product and process quality profiles

A hierarchical model for characterising spatial wafer variations

Churn detection via customer profile modelling

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