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In technologies affected by variability, the detection status of a small-delay fault may vary among manufactured circuit instances. The same fault may be detected, missed or provably undetectable in different circuit instances. We introduce the first complete flow to accurately evaluate and systematically maximize the test quality under variability. As the number of possible circuit instances is infinite, we employ statistical analysis to obtain a test set that achieves a fault-efficiency target with an user-defined confidence level. The algorithm combines a classical path-oriented test-generation procedure with a novel waveformaccurate engine that can formally prove that a small-delay fault is not detectable and does not count towards fault efficiency. Extensive simulation results demonstrate the performance of the generated test sets for industrial circuits affected by uncorrelated and correlated variations. Preprint General Copyright NoticeThis article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. This is the author's "personal copy" of the final, accepted version of the paper published by IEEE. Abstract-In technologies affected by variability, the detection status of a small-delay fault may vary among manufactured circuit instances. The same fault may be detected, missed or provably undetectable in different circuit instances. We introduce the first complete flow to accurately evaluate and systematically maximize the test quality under variability. As the number of possible circuit instances is infinite, we employ statistical analysis to obtain a test set that achieves a fault-efficiency target with an user-defined confidence level. The algorithm combines a classical path-oriented test-generation procedure with a novel waveformaccurate engine that can formally prove that a small-delay fault is not detectable and does not count towards fault efficiency. Extensive simulation results demonstrate the performance of the generated test sets for industrial circuits affected by uncorrelated and correlated variations.

Pseudo-exhaustive test completely verifies all output functions of a combinational circuit, which provides a high coverage of non-target faults and allows an efficient on-chip implementation. To avoid long test times caused by large output cones, partial pseudo-exhaustive test (P-PET) has been proposed recently. Here only cones with a limited number of inputs are tested exhaustively, and the remaining faults are targeted with deterministic patterns. Using P-PET patterns for built-in diagnosis, however, is challenging because of the large amount of associated response data. This paper presents a built-in diagnosis scheme which only relies on sparsely distributed data in the response sequence, but still preserves the benefits of P-PET.

Modern diagnosis algorithms are able to identify the defective circuit structure directly from existing fail data without being limited to any specialized fault models. Such algorithms however require test patterns with a high defect coverage, posing a major challenge particularly for embedded testing. In mixed-mode embedded test, a large amount of pseudorandom (PR) patterns are applied prior to deterministic test pattern. Partial Pseudo-Exhaustive Testing (P-PET) replaces these pseudo-random patterns during embedded testing by partial pseudo-exhaustive patterns to test a large portion of a circuit fault-model independently. The overall defect coverage is optimized compared to random testing or deterministic tests using the stuck-at fault model while maintaining a comparable hardware overhead and the same test application time. This work for the first time combines P-PET with a fault model independent diagnosis algorithm and shows that arbitrary defects can be diagnosed on average much more precisely than with standard embedded testing. The results are compared to random pattern testing and deterministic testing targeting stuck-at faults.