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A hybrid method for feature selection in the context of Alternate Test

Author(s): G. Leger, M. Barragan

Doc. Source: International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD'15)

Publisher: IEEE

Pages: 1-4

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.