A parametric test method for analog components in integrated mixed-signal circuits
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Hard-Fault Detection and Diagnosis During the Application of Model-Based Data Converter Testing
Journal of Electronic Testing: Theory and Applications
An approach to linear model-based testing for nonlinear cascaded mixed-signal systems
Proceedings of the Conference on Design, Automation and Test in Europe
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In this paper, we present methods for constructing optimal tests to detect structural faults in analog integrated circuits in the presence of process variation. The analog test determination problem is formulated as selecting an optimal subset from an initial large set of tests with optimality criteria defined in terms of fault coverage and fault separation on a given fault set. The process variation may be represented either deterministically by box constraints or statistically as random variables. Each of these representations require different methods for computing the detectabilities. In the deterministic case, the detectability measures are computed by a combination of analytical and numerical optimization techniques. Such an approach helps reduce the number of simulations by up to three times over traditional Monte Carlo methods. This approach produces more compact test sets compared to a linear sensitivity analysis while being closer in accuracy to the Monte Carlo method. In the statistical case, the detectability measures are computed as separation distances between the good and faulty distributions. These distributions represented nonparametrically are generated by traditional Monte Carlo techniques. Once the deterministic or statistical detectabilities are computed for the entire test set, a test compaction step is performed which is a covering problem. On solving this covering problem eve get a test set with optimal fault coverage and fault separation