Every well-known march test for memories was generated to efficiently achieve 100% coverage of a target set of fault types. The question we pursue is: What to do if 100% coverage of the given target set cannot be achieved under tight constraints on test cost? We first study an obvious option: Remove some fault types from the given target set until a new or well-known test can cover 100% of the remaining fault types under the given test cost constraint. We find that this approach leaves significant room for improvement. We then pursue a different option and develop a new method which uses the original target set of fault types and generates a march test that maximizes the fault coverage under the given tight constraint on test cost. Our method generates fault-coverage maximizing tests for a wide range of target sets of fault types. A comparison with well-known march tests with equal lengths demonstrates that our new march tests provide significantly higher coverage for various sets of fault types. Importantly, our new march tests provide graceful decrease in fault coverage as we tighten constraints on test length. Hence our method and new march tests enable tradeoffs between test quality and test cost and provide a new direction of memory test research focused on fault-coverage-maximization.
Authors: Yun Feng, Yunkun Lin, Lou Yunfei, Lei Gao, Vaibhav Gera, Boxuan Li, Vennela Chowdary Nekkanti, Aditya Rajendra Pharande, Kunal Sheth, Meghana Thommondru, Guizhong Ye, Sandeep Gupta
DOI: https://doi.org/10.1109/itc50671.2022.00066
Publish Year: 2022
