A virtual metrology system based on least angle regression and statistical clustering. (17th September 2012)

• Record Type: Journal Article
• Authors: Susto, Gian Antonio; Beghi, Alessandro; Borgoni, Riccardo; Deldossi, Laura; Radaelli, Luigi; Zappa, Diego
• Is Part Of: Applied stochastic models in business and industry. Volume 29:Number 4(2013:Jul./Aug.)
• Page Start: 362
• Page End: 376
• Publication Date: 2012-09-17
• Subjects: Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.1948
• Languages: English
• ISSNs: 1524-1904

Abstract

<abstract abstract-type="main" id="asmb1948-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="asmb1948-para-0001">In semiconductor manufacturing plants, monitoring physical properties of all wafers is crucial to maintain good yield and high quality standards. However, such an approach is too costly, and in practice, only few wafers in a lot are actually monitored. Virtual metrology (VM) systems allow to partly overcome the lack of physical metrology. In a VM scheme, tool data are used to predict, for every wafer, metrology measurements. In this paper, we present a VM system for a chemical vapor deposition (CVD) process. On the basis of the available metrology results and of the knowledge, for every wafer, of equipment variables, it is possible to predict CVD thickness. In this work, we propose a VM module based on least angle regression to overcome the problem of high dimensionality and model interpretability. We also present a statistical distance‐based clustering approach for the modeling of the whole tool production. The proposed VM models have been tested on industrial production data sets. Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract>