Diagnosis of Arbitrary Defects Using Neighborhood Function Extraction

Abstract

We present a methodology for diagnosing arbitrary defects in digital integrated circuits (ICs). Rather than using one or a set of fault models in a cause-effect or effect-cause approach, our methodology derives defect behavior from the test set, the circuit and its response, and the physical neighbors that surround a potential defect location. The defect locations themselves are identified using a model-independent stage. The methodology enables accurate identification of defect location and behavior through validation via simulation using passing and additional diagnostic test patterns. A byproduct of our methodology is the distinction that can be made among stuck-fault equivalencies which results in improved diagnostic resolution. Several types of shorts and opens are used to demonstrate the applicability of our approach to the diagnosis of arbitrary defects.

Physically-Aware Analysis of Systematic Defects in Integrated Circuits

“…In the first experiment, the physically-aware diagnosis methodology described in [35,134] is applied to the population of failures described in Section 4.4.1. In [35,134], both passing and failing tester patterns are analyzed at the layout level to identify potential defect site(s) and conditions for defect/failure activation.…”

“…In [35,134], both passing and failing tester patterns are analyzed at the layout level to identify potential defect site(s) and conditions for defect/failure activation. At the layout level, nets that are physically close to the defect site (i.e., neighbors) that are found to influence defect activation are identified.…”

PADRE: Physically-Aware Diagnostic Resolution Enhancement

“…The existence of an inconsistent state is likely an indication that the candidate is not actually a good candidate. While it is possible that a defective location can behave inconsistently, past work has shown that this feature is an excellent discriminator [1,4,10,11,12]. …”

“…In [8,9], it is suggested that candidates detected by more tester-passing patterns are less likely to capture the actual defective location(s). Other work reveals that the same neighborhood state of a good candidate should not be observed in both the Tester-Pass-Simulation-Fail (TPSF) and the Tester-Fail-Simulation-Fail (TFSF) patterns [1,4,10,11,12]. If a neighborhood state appears in both TPSF and TFSF patterns, the candidate is said to be inconsistent and is likely incorrect [1,4,10,11,12].…”

“…In past work [1,4,10,11,12], it has been shown that the consistency check was very adept at identifying bad 1 Although simulation data is used in this work, it should be noted that it is not integral and is only used in order to verify accuracy.…”