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.

• Type: Conference
• Year: 2009
• Where: DATE
• Authors: Haralampos-G. D. Stratigopoulos, Salvador Mir, Yiorgos Makris
• Keywords: DATE 2009, Hardware, Lowcost Test Measurement, Pass/fail Test Decision, Test Decision