Optimal wafer site selection using forward selection component analysis

Prakash, Pks; Honari, Bahman; Johnston, Adrian; McLoone, Seán

doi:10.1109/asmc.2012.6212875

Cited by 19 publications

(23 citation statements)

References 8 publications

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“…As a final application example, we evaluate the performance of SPBR, MPBR, R-SPBR and R-MPBR as alternatives to FSCA for the semiconductor wafer metrology site optimisation methodology developed in [24]. The pertinent details are as follows.…”

Section: Wafer Site Optimisationmentioning

confidence: 99%

“…Hence, X ∈ R 316×50 and the site selection problem equates to selecting the subset of columns of X that best describe X. For a detailed description of the problem statement, solution methodology and case study dataset the reader is referred to [24].…”

Section: Wafer Site Optimisationmentioning

confidence: 99%

“…FSCA was initially introduced in the context of Optical Emission Spectroscopy (OES) data analysis of plasma etch processes [23] where isolating a small number of wavelengths is important for understanding the underlying plasma chemistry. More recently, FSCA has been found to be a particularly effective tool for optimising measurement site selection for spatial wafer metrology in semiconductor manufacturing [24]. The method works by iteratively deriving a set of orthogonal components which are a function of only a subset of the original variables, and which sequentially maximize the explained variance.…”

Section: Introductionmentioning

confidence: 99%

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Forward Selection Component Analysis: Algorithms and Applications

Puggini

McLoone

2017

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Principal Component Analysis (PCA) is a powerful and widely used tool for dimensionality reduction. However, the principal components generated are linear combinations of all the original variables and this often makes interpreting results and root-cause analysis difficult. Forward Selection Component Analysis (FSCA) is a recent technique that overcomes this difficulty by performing variable selection and dimensionality reduction at the same time. This paper provides, for the first time, a detailed presentation of the FSCA algorithm, and introduces a number of new variants of FSCA that incorporate a refinement step to improve performance. We then show different applications of FSCA and compare the performance of the different variants with PCA and Sparse PCA. The results demonstrate the efficacy of FSCA as a low information loss dimensionality reduction and variable selection technique and the improved performance achievable through the inclusion of a refinement step.

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Section: Wafer Site Optimisationmentioning

confidence: 99%

Section: Wafer Site Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forward Selection Component Analysis: Algorithms and Applications

Puggini

McLoone

2017

IEEE Trans. Pattern Anal. Mach. Intell.

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“…This dataset, which is described in detail in [1], consists of wafer metrology data from a semiconductor manufacturing process for a set of 316 wafers. The goal is to improve the efficiency of wafer profile monitoring by reducing the number of measured sites from a candidate set of 50 sites, without discarding valuable information.…”

Section: Real Dataset 1: Wafer Site Optimizationmentioning

confidence: 99%

“…As described in [1], the unmeasured sites can be estimated from the measured sites using a linear model with the selected sites as regressors. Table 4 provides a comparison of the performance of the seven sites selected by FSCA and BECA with regard to their ability to predict the 43 unmeasured sites on each wafer.…”

Section: Real Dataset 1: Wafer Site Optimizationmentioning

confidence: 99%

Mean Squared Error vs. Frame Potential for Unsupervised Variable Selection

Zocco

McLoone

2017

Communications in Computer and Information Science

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Abstract. Forward Selection Component Analysis (FSCA) provides a pragmatic solution to the NP-hard unsupervised variable selection problem, but is not guaranteed to be optimal due to the multi-modal nature of the mean squared error (MSE) selection metric used. Frame potential (FP) is a metric that has recently been shown to yield near-optimal greedy sensor selection performance for linear inverse problems. This paper explores if FP offers similar benefits in the unsupervised variable selection context. In addition, the backward elimination counterpart of FSCA is introduced for the first time (BECA) and compared with forward and backward FP based variable selection on a number of simulated and real world datasets. It is concluded that FP does not improve on FSCA and that while BECA yields comparable results to FSCA it is not a competitive alternative due to its much higher computational complexity.

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