Embedded Test for Highly Accurate Defect Localization

Abstract

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.