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DATE
2009
IEEE

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

9 years 6 months ago
Enrichment of limited training sets in machine-learning-based analog/RF test
Abstract— This paper discusses the generation of informationrich, 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 lowcost 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 tradeoff between test escapes, yield loss, and percentage of retested devices.
Haralampos-G. D. Stratigopoulos, Salvador Mir, Yio
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where DATE
Authors Haralampos-G. D. Stratigopoulos, Salvador Mir, Yiorgos Makris
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