A parametric test method for analog components in integrated mixed-signal circuits
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Mixed-Signal Circuit Classification in a Pseudo-Random Testing Scheme
Journal of Electronic Testing: Theory and Applications
An Approach to the Classification of Mixed-Signal Circuits in a Pseudorandom Testing Scheme
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Parametric variability analysis for multistage analog circuits using analytical sensitivity modeling
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
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New methods for analog fault detection and for the selection of measurements for analog testing (wafer probe or final testing) are presented. Using Bayes' rule, the information contained in the measurement data and the information of the a priori probabilities of a circuit being fault free or faulty are converted into a posteriori probabilities and used for fault detection in analog integrated circuits, with a decision criterion that considers the statistical tolerances and mismatches of the circuit parameters. An adaptive formulation of the a priori probabilities is given that updates their values according to the results of the testing and fault detection. In addition, a systematic method is proposed for the optimal selection of the measurement components so as to minimize the probability of an erroneous test decision. Examples of DC wafer-probe testing as well as production testing using the power-supply current spectrum are given that demonstrate the effectiveness of the algorithms