| KEYWORD |
Machine Learning and Computer Aided Design
keywords CLASSIFICATION ALGORITHMS, COMPUTATIONAL INTELLIGENCE, COMPUTER AIDED DESIGN, MACHINE LEARNING, REGRESSION ALGORITHMS, TEST
Reference persons RICCARDO CANTORO, GIOVANNI SQUILLERO
Research Groups GR-05 - ELECTRONIC CAD & RELIABILITY GROUP - CAD
Thesis type EXPERIMENTAL
Description The thesis will tackle a quite practical and important application of machine learning. The goal is to design a methodology to be integrated in existing industrial workflows: building a model able to predict the good/fail classification of electronic devices based on manufacturing data from wafer tests. In the specific context the different errors (false positive or false negative) have different impacts on the quality of the result. For a limited number of devices, the maximum frequencies of functional patterns is also available, and this information could be used for building a regression model.
Required skills Required: programming skill
Desired: python3, statistics
Deadline 31/01/2019
PROPONI LA TUA CANDIDATURA