Title :
A hybrid method for feature selection in the context of alternate test
Author :
Gildas Leger;Manuel J. Barragan
Author_Institution :
Instituto de Microlectró
Abstract :
Machine-learning test strategy has been developed in the last decade as an alternative to costly specification-driven tests for Analog, Mixed-Signal and RF circuits (AMS-RF). The concept is simple: powerful algorithms are used to map simple measurements onto specifications. But the proper execution requires an information-rich input space. This paper presents an efficient hybrid algorithm to select the best subset of signatures (or features) among a large number of candidates and shows how it can be applied to eventually propose the development of new ones.
Keywords :
"Correlation","Computational modeling","Radio frequency","Testing","Mathematical model","Training","Accuracy"
Conference_Titel :
Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2015 International Conference on
DOI :
10.1109/SMACD.2015.7301707