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Enrichment of limited training sets in machine-learning-based analog/RF Test

Auteur(s) : H. Stratigopoulos, S. Mir, Y. Makris

Doc. Source: Design, Automation and Test in Europe Conference (DATE’09)

Publisher : IEEE

Pages : 1668 - 1673

This paper discusses the generation of information-rich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of low-cost measurements to a set of device performances and (b) grade such tests with parts per million (PPM) accuracy. This is achieved by sampling a non-parametric estimate of the joint probability density function of measurements and performances. Our case study is an ultra-high frequency receiver front-end and the focus of the paper is to learn the mapping between a low-cost test measurement pattern and a single pass/fail test decision which reflects compliance to all performances. The small fraction of devices for which such a test decision is prone to error are identified and retested through standard specification-based test. The mapping can be set to explore thoroughly the trade-off between test escapes, yield loss, and percentage of retested devices.