An approach for relating model parameter variabilities to process fluctuations

Power, J.A.; Mathewson, A.; Lane, W.A.

doi:10.1109/icmts.1993.292892

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…is the sum of normalized squared scores for the th observation and is defined as ( [17]): (5) where is the scores matrix cutoff at PCs and is the th vector in . Equation (5) is used first for NOC data, thus defining a statistic for each measurement.…”

Section: B the Mbpca Algorithmmentioning

confidence: 99%

“…Unfortunately, commonly used multivariate methods such as principal component analysis (PCA) [2], [3] and partial least squares (PLS) (e.g., see [4]) are not designed to deal directly with data that are nonlinearly correlated, and therefore tend to perform less effectively in such cases. Indeed, conventional PCA has been used to monitor 0894-6507/02$17.00 © 2002 IEEE microelectronic manufacturing with limited success (e.g., see [5]). As a consequence, many approaches have been suggested to extend the monitoring capabilities of multivariate statistical methods, such as nonlinear PCA [6], neural networks (e.g., see [7] and [8]) and genetic programming [9].…”

Section: Introductionmentioning

confidence: 99%

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MBPCA application for fault detection in NMOS fabrication

Lachman-Shalem¹,

Haimovitch²,

Shauly³

et al. 2002

IEEE Trans. Semicond. Manufact.

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This paper describes the application of model-based principal component analysis (MBPCA) to the identification and isolation of faults in NMOS manufacture. In MBPCA, multivariate statistics are applied to the analysis of the portion of the data variance that is unexplained by models based on material and energy balances carried out on the unit operations used in manufacture. It is demonstrated that the failure detection and isolation performance achievable using the model-based procedure exceeds that of commonly used univariate SPC or conventional PCA approaches. Index Terms-Model-based fault diagnosis, model-based PCA, NMOS device fabrication.

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Section: B the Mbpca Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MBPCA application for fault detection in NMOS fabrication

Lachman-Shalem¹,

Haimovitch²,

Shauly³

et al. 2002

IEEE Trans. Semicond. Manufact.

View full text Add to dashboard Cite

show abstract

“…Unfortunately, commonly used multivariate methods such as principal component analysis (PCA) (Jackson 1991;Rencher 1995) and partial least squares (PLS) are not designed to deal directly with data that are nonlinearly correlated, and therefore tend to perform less effectively in such cases. Indeed, conventional PCA has been used to monitor microelectronic manufacturing with limited success (e.g., Power et al, 1993;Chen et. al., 2000).…”

Section: Introductionmentioning

confidence: 99%

Fault detection in CMOS manufacturing using MBPCA

et al. 2000

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This paper describes the application of model-based principal component analysis (MBPCA) to the identification and isolation of faults in CMOS manufacture. Some of the CMOS fabrication processing steps are well understood, with first principles mathematical models available, which can describe the physical and chemical phenomena that take place. The fabrication of the device using a known industrial process is therefore first modeled "ideally," using ATHENA and MATLAB. Detailed furnace models are used to investigate the effect of errors in furnace control on the device fabrication and the subsequent effect on the device electrical properties. This models the distribution of device properties resulting from processing a stack of wafers in a furnace, and allows faults and production errors to be simulated for analysis. The analysis is performed using MBPCA, which has been shown to improve fault-detection resolution for batch processes. The diagnosis method is demonstrated on an industrial NMOS transistor fabrication process with faults introduced in places where they might realistically occur.

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“…In the past, various attempts were made to link process parameters to circuit and system performance. Power et al [15] used PCA and VARIMAX approach to correlate process parameters to device model parameters. However, their approach does not provide direct link between process parameters and circuit performance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Link Process Parameters to BSIM Model Parameters

Mande

Chandorkar

Hsaio

et al. 2009

IEEE Trans. Semicond. Manufact.

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Abstract-In this paper, we demonstrate a methodology to link process parameters to BSIM model parameters. Here, we have combined well-known statistical methods like principal component analysis (PCA), design of experiments (DOE), and response surface methodology (RSM) to bridge the missing link between process parameters and model parameters. The proposed methodology uses the concept of a correlation matrix, which transforms the process level information to the device and circuit level information through the BSIM model parameters. The proposed methodology has been successfully implemented on an advanced CMOS process. Our results show a strong linear correlation for the data obtained from two techniques namely TCAD technique and the standard HSPICE simulation technique. In both cases the process conditions were kept identical for comparison.Index Terms-CMOS, design of experiments, principal component analysis, process variations, response surface methodology, variability.

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An approach for relating model parameter variabilities to process fluctuations

Cited by 16 publications

References 8 publications

MBPCA application for fault detection in NMOS fabrication

MBPCA application for fault detection in NMOS fabrication

Fault detection in CMOS manufacturing using MBPCA

A Novel Approach to Link Process Parameters to BSIM Model Parameters

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