Non-RF to RF Test Correlation Using Learning Machines: A Case Study

The authors present a case study that employs production test data from an RF device to assess the effectiveness of four different methods in predicting the pass/fail labels of fabricated devices based on a subset of performances and, thereby, in decreasing test cost. The device employed is a zero-IF down-converter for cell-phone applications and the four methods range from a sample maximum-cover algorithm to an advanced ontogenic neural network. The results indicate that a subset of non-RF performances suffice to predict correctly the pass/fail label for the vast majority of the devices and that the addition of a few select RF performances holds great potential for reducing misprediction to industrially acceptable levels. Based on these results, the authors then discuss enhancements and experiments that will further corroborate the utility of these methods within the cost realities of analog/RF production testing.