Least angle regression for semiconductor manufacturing modeling

In semiconductor manufacturing plants, monitoring physical properties of all wafers is fundamental in order 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. We propose a VM module based on LARS to overcome the problem of high dimensionality and model interpretability. The proposed VM models have been tested on industrial production data sets.