Name
Abstract | ||
|---|---|---|
Numerous machine-learning-based test methodologies have been proposed in recent years as a fast alternative to the standard functional testing of mixed-signal/RF integrated circuits. While the test error probability of these methods is rather low, it is still considered prohibitive for accurate production testing. In this paper, we demonstrate how to minimize this test error probability and, thus, how to bridge the accuracy of functional and machine-learning-based test methods. The underlying idea is to measure the confidence of the machinelearning- based test decision and retest the small fraction of circuits for which this confidence is low via standard functional test. Through this approach, the majority of circuits are tested using fast machine-learning-based tests, which, nevertheless, are equivalent to the standard functional ones with regards to test error probability. By varying the acceptable confidence level, the proposed method enables exploration of the trade-off between test time and test accuracy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/VTS.2006.24 | Berkeley, CA |
Keywords | Field | DocType |
i. introduction while functional testing of mixed-signal/rf circuits is highly accurate,test error probability,standard functional test,machine-learning-based test method,wherein the results,test decision,it involves elaborate measurement procedures that incur an often-prohibitive cost. as an alternative,numerous machine-learning-based test methodology,standard functional testing,machine-learning-based test,test time,test accuracy,machine-learning-based mixed-signal testing,machine learning inspired a new test paradigm,acceptable confidence level,learning artificial intelligence,machine learning,functional testing,integrated circuit,test methods,error probability,confidence level | Automatic test pattern generation,Mixed signal testing,Computer science,Bridging (networking),Functional testing,Electronic engineering,Artificial intelligence,Electronic circuit,Test compression,Confidence interval,Integrated circuit,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2514-8 | 4 | 0.69 |
References | Authors | |
24 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haralampos-G. D. Stratigopoulos | 1 | 252 | 28.06 |
| Yiorgos Makris | 2 | 1365 | 107.21 |