Test Primitive:A Straightforward Method To Decouple March

TL;DR

This paper introduces a new test primitive for analyzing the March algorithm, enabling decoupling of cell states from operations, which enhances the analysis's flexibility, scalability, and theoretical robustness.

Contribution

It proposes a novel test primitive that overcomes limitations of existing fault modeling methods for the March algorithm, with proven completeness and uniqueness.

Findings

Test primitives effectively decouple cell states from operations.

The proposed primitives are complete, unique, and concise.

Enhanced analysis flexibility and scalability for the March algorithm.

Abstract

The academic community has made outstanding achievements in researching the March algorithm. However, the current fault modeling method, which centers on fault primitives, cannot be directly applied to analyzing the March algorithm. This paper proposes a new test primitive. The test primitives, which decouple the cell states from sensitization and detection operations, describe the common features that must be possessed for the March algorithm to detect corresponding faults, forming a highly flexible and scalable March algorithm analysis unit. The theoretical analysis proves that the test primitives demonstrate completeness, uniqueness, and conciseness. On this foundation, the utilization of test primitives within the March analysis procedure is elucidated.