Test Primitives: The Unified Notation for Characterizing March Test Sequences

March algorithms are essential for detecting functional memory faults, characterized by their linear complexity and adaptability to emerging technologies. However, the increasing complexity of fault types presents significant challenges to existing fault detection models regarding analytical efficiency and adaptability. This article introduces the test primitive (TP), a unified notation that characterizes March test sequences through a novel methodology that decouples fault detection operations from sensitization states. The proposed TP achieves platform independence and seamless integration of fault models, supported by rigorous theoretical proofs. These proofs establish the fundamental properties of the TP in terms of completeness, uniqueness, and conciseness, providing a theoretical foundation that ensures the decoupling method reduces the computational complexity of March algorithm analysis to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(1)$</tex-math> </inline-formula>. This reduction is analogous to Karnaugh map simplification in digital logic while enabling millisecond-level automated analysis. Experimental results demonstrate that the proposed method significantly enhances both analyzable fault coverage (FC) and detection accuracy, thereby addressing critical limitations of existing fault detection models.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2025-06-17
DOI
https://doi.org/10.1109/tvlsi.2025.3577448
Citations
2
Primary Topic
Real-time simulation and control systems
Type
article
Field-Weighted Citation Impact
2.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Test Primitives: The Unified Notation for Characterizing March Test Sequences

Weikun Xie, Yindong Xiao, Houjun Wang, Susong Yang et al.
2 citations
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Real-time simulation and control systems
2.43
article

Test Primitives: The Unified Notation for Characterizing March Test Sequences

Weikun Xie, Yindong Xiao, Houjun Wang, Susong Yang, Ruiqi Zhu
article en
2 citations

Abstract

March algorithms are essential for detecting functional memory faults, characterized by their linear complexity and adaptability to emerging technologies. However, the increasing complexity of fault types presents significant challenges to existing fault detection models regarding analytical efficiency and adaptability. This article introduces the test primitive (TP), a unified notation that characterizes March test sequences through a novel methodology that decouples fault detection operations from sensitization states. The proposed TP achieves platform independence and seamless integration of fault models, supported by rigorous theoretical proofs. These proofs establish the fundamental properties of the TP in terms of completeness, uniqueness, and conciseness, providing a theoretical foundation that ensures the decoupling method reduces the computational complexity of March algorithm analysis to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(1)$</tex-math> </inline-formula>. This reduction is analogous to Karnaugh map simplification in digital logic while enabling millisecond-level automated analysis. Experimental results demonstrate that the proposed method significantly enhances both analyzable fault coverage (FC) and detection accuracy, thereby addressing critical limitations of existing fault detection models.

IEEE Transactions on Very Large Scale Integration (VLSI) SystemsVol. 33(9)
University of Electronic Science and Technology of China (CN)
Shantou Science and Technology Project
Openalex Percentile: Top 11%
Real-time simulation and control systems
2.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.