Active Learning for Overlay Prediction in Semi-conductor Manufacturing

Master thesis (2018)

Authors

K.A. van Garderen Electrical Engineering, Mathematics and Computer Science

Contributors

D.M.J. Tax (mentor)
Alexander Ypma (mentor)
M.J.T. Reinders (graduation committee member)
A.E. Zaidman (graduation committee member)
Maialen Larranaga (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Karin van Garderen
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Published Date

18-05-2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In the manufacturing of semi-conductor devices there is a constant demand for increasing precision and yield. Measuring and controlling overlay errors is essential in this process, but these measurements are difficult and costly. Predictive models can be used as an addition to measurements, but they required labelled data for training. To achieve maximal performance with few measurements, active learning methods are explored that apply a sampling strategy to select which wafers to measure. The predictive model is a partial least squares regression, which is also used to provide informative visualizations of the high-dimensional data.