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    Plasma Etch Process Virtual Metrology using Aggregative Linear Regression

    Prakash, PKS and McLoone, Sean F. (2011) Plasma Etch Process Virtual Metrology using Aggregative Linear Regression. In: Third International Conference of Soft Computing and Pattern Recognition (SocPaR 2011), 14-16 October 2011, Dalian, China.

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    To enhance product quality semiconductor manufacturing industries are increasing the amount of metrology information collected during manufacturing processes. This increase in information has provided companies with many opportunities for enhanced process monitoring and control. However, the increase in information also posses challenges as it is quite common now to collect many more measurements than samples from a process leading to ill-conditioned datasets. Illconditioned datasets are very common in semiconductor manufacturing industries where infrequent sampling is the norm. It is therefore critical to be able to quantify virtual metrology models developed from such data sets. This paper presents an aggregative linear regression methodology for modeling that allows the generation of confidence intervals on the predicted outputs. The aggregation enhances the robustness of the linear models in terms of process variation and model sensitivity towards prediction. Also, to deal with the large number of candidate process variables, variable selection methods are employed to reduce the dimensionality and computational efforts associated with building virtual metrology models. In the paper three methods for variable selection are evaluated in conjunction with aggregative linear regression (ALR). The proposed methodology is tested on a benchmark semiconductor plasma etch process dataset and the results are compared with state-ofart multiple linear regression (MLR) and Gaussian Process Regression (GPR) VM models.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Virtual metrology; Aggregative Linear Regression; Forward Stepwise Regression; Decision Trees;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 3650
    Depositing User: Sean McLoone
    Date Deposited: 08 May 2012 15:07
    Refereed: Yes
      Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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