Print Email Facebook Twitter Active Learning for Overlay Prediction in Semi-conductor Manufacturing Title Active Learning for Overlay Prediction in Semi-conductor Manufacturing Author van Garderen, Karin (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Tax, David (mentor) Ypma, Alexander (mentor) Reinders, Marcel (graduation committee) Zaidman, Andy (graduation committee) Larranaga, Maialen (graduation committee) Degree granting institution Delft University of Technology Date 2018-05-18 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. Subject Active LearningRegressionVisualization To reference this document use: http://resolver.tudelft.nl/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66 Part of collection Student theses Document type master thesis Rights © 2018 Karin van Garderen Files PDF 180508_thesis_kvangarderen.pdf 5.75 MB Close viewer /islandora/object/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66/datastream/OBJ/view