| Algorithms for the diagnosis of parametric faults in analog and mixed-signal circuits were proposed. The effects of measurement noise and other inaccuracies that accompany analog measurements, on the accuracy of the diagnosed parameters, was analyzed. A methodology for approximating the relationship between the circuit parameters to be diagnosed and the measurements made on the circuit, using nonlinear regression techniques, was proposed. This results in significant reduction in the computational work required for diagnosis. In cases that complete diagnosis was not possible, a procedure to generate optimized tests to aid diagnosis was proposed. The test generation methodology explicitly accounts for the effects of measurement noise for robust test generation. The algorithms for diagnosis and test generation were applied to the problem of diagnosing the causes of parametric yield loss in analog integrated circuit manufacturing. The algorithms were also applied to reducing the production test time for analog-to-digital converters.; To extend the proposed algorithms to larger systems, two heuristic-based approaches were explored. A methodology to model the fault-free behavior of parts of the circuit, to isolate faults, was proposed. Methods to model fault effects in mixed-signal systems using error waveforms were also proposed for isolating faults to either the digital or the analog portion of a mixed-signal circuit. Experimental results to verify the methods are presented and avenues for future research are discussed. |
