Test compaction for small-delay defects using an effective path selection scheme

Author:

Xiang Dong1,Li Jianbo2,Chakrabarty Krishnendu3,Lin Xijiang4

Affiliation:

1. Tsinghua University, China

2. Tsinghua University, Beijing, China

3. Duke University, Durham, NC

4. Mentor Graphics Corp, Wilsonville, OR

Abstract

Testing for small-delay defects (SDDs) requires fault-effect propagation along the longest testable paths. However, identification of the longest testable paths requires high CPU time, and the sensitization of all such paths leads to large pattern counts. Dynamic test compaction for small-delay defects is therefore necessary to reduce test-data volume. We present a new technique for identifying the longest testable paths through each gate in order to accelerate test generation for SDDs. The resulting test patterns sensitize the longest testable paths that pass through each SDD site. An efficient dynamic test compaction method based on structural analysis is presented to reduce the pattern count substantially, while ensuring that all the longest paths for each SDD are sensitized. Simulation results for a set of ISCAS 89 and IWLS 05 benchmark circuits demonstrate the effectiveness of this method.

Funder

National Natural Science Foundation of China

National Education Ministry

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Cited by 26 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Test Compaction Using (k, 1)-Cycle Tests;2024 IEEE 42nd VLSI Test Symposium (VTS);2024-04-22

2. Dynamic Test Compaction of a Compressed Test Set Shared Among Logic Blocks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-01

3. Test Compression for Launch-on-Capture Transition Fault Testing;ACM T DES AUTOMAT EL;2024

4. Testability Evaluation for Local Design Modifications;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-01

5. Longest Path Selection Based on Path Identifiers;IEEE Access;2024

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