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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...
Several existing methodologies have leveraged the correlation between the non-RF and the RF performances of a circuit in order to predict the latter from the former and, thus, reduce test cost. While this form of specification test compaction eliminates the need for expensive RF measurements, it also comes at the cost of reduced test accuracy, since the retained non-RF measurements and pertinent correlation...
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