Learn to Generate Complementary March Tests

Ruiqi Zhu

doi:10.1109/tvlsi.2026.3699930

Learn to Generate Complementary March Tests

2026-06-11

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DOI：10.1109/tvlsi.2026.3699930

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March tests require balancing fault coverage (FC) and test length in embedded memory testing. This brief proposes a novel reinforcement-learning (RL)-based framework that learns optimal March tests by exploiting undetected fault statistics. First, we analyze March tests using a scalable state-tuple-based fault analyzer. Then, we formulate March test generation as a problem to maximize expected FC (EFC) under budget limits. By modeling this as a Markov decision process (MDP), we leverage deep Q-learning to find the optimal operation sequences in March tests, with rewards derived from the proposed fault analyzer. Experimental results demonstrate that our framework generates complementary March tests achieving complete residual FC across a broader range of fault models than the existing methods, with generation efficiency significantly improved through fault analysis completing in milliseconds.

Keywords:

March algorithm

memory testing

reinforcement learning (RL)

test escape

test primitive

Learn to Generate Complementary March Tests

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