Random Pattern Testability

TLDR

In self‑testing with random inputs, verifying test quality by computing fault coverage is challenging, and full‑fault simulation is impractical due to large logic volumes and high CPU time. The authors propose a new approach to address this challenge. They introduce a fast analytical method for estimating fault coverage that outperforms simulation. When coverage falls below a threshold, the method identifies random‑pattern‑resistant faults, enabling logic modifications that raise overall coverage.

Abstract

A major problem in self testing with random inputs is verification of the test quality, i.e., the computation of the fault coverage. The brute-force approach of using full-fault simulation does not seem attractive because of the logic structure volume, and the CPU time encountered. A new approach is therefore necessary. This paper describes a new analytical method of computing the fault coverage that is fast compared with simulation. If the fault coverage falls below a certain threshold, it is possible to identify the ``random-pattern-resistant'' faults, modify the logic to make them easy to detect, and thus, increase the fault coverage of the random test.