The objective of this work is the creation of predictive models that can forecast the electrical or physical
parameters of wafers using data collected from the relevant processing tools. In this way, direct measurements from the
wafer can be minimized or eliminated altogether, hence the term "virtual" metrology. Challenges include the selection of
the appropriate process step to monitor, the pre-treatment of the raw data, and the deployment of a Virtual Metrology
Model (VMM) that can track a manufacturing process as it ages. A key step in any VM application is dimensionality
reduction, i.e. ensuring that the proper subset of predictors is included in the model. In this paper, a software tool
developed with MATLAB is demonstrated for interactive data prescreening and selection. This is combined with a
variety of automated statistical techniques. These include step-wise regression and genetic selection in conjunction with
linear modeling such as Principal Component Regression (PCR) and Partial Least Squares (PLS). Modeling results
based on industrial datasets are used to demonstrate the effectiveness of these methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.