Scan Chain Diagnosis Based on Unsupervised Machine Learning

Abstract

Scan-based testing has proven to be a cost-effective method for achieving good test coverage in digital circuits. It was reported in prior papers that about 30% to 50% of all failing die were due to defects that cause scan chains to fail [1][2]. Therefore, scan chain failure diagnosis is very important to improve yield. The previously proposed methods of chain diagnosis were primarily based on either deterministic fault models and simulation algorithms or probabilistic analysis. To handle hard-to-model defect behaviors more robustly, in this paper, we propose a new scan chain diagnosis algorithm based on unsupervised machine learning. Its application on "scannable memory designs" (SMD) is demonstrated to illustrate the effectiveness of the proposed algorithm.