Tsinghua Science and Technology  2021, Vol. 26 Issue (1): 1-8    doi: 10.26599/TST.2019.9010046
 Special Issue on Reliability and Security
Efficient Static Compaction of Test Patterns Using Partial Maximum Satisfiability
Huisi Zhou, Dantong Ouyang, Liming Zhang*
∙ Huisi Zhou, Dantong Ouyang, and Liming Zhang are with the Laboratory of Symbol Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China. E-mail: 1285162366@qq.com; ouyangdantong@163.com;

Abstract

Static compaction methods aim at finding unnecessary test patterns to reduce the size of the test set as a post-process of test generation. Techniques based on partial maximum satisfiability are often used to track many hard problems in various domains, including artificial intelligence, computational biology, data mining, and machine learning. We observe that part of the test patterns generated by the commercial Automatic Test Pattern Generation (ATPG) tool is redundant, and the relationship between test patterns and faults, as a significant information, can effectively induce the test patterns reduction process. Considering a test pattern can detect one or more faults, we map the problem of static test compaction to a partial maximum satisfiability problem. Experiments on ISCAS89, ISCAS85, and ITC99 benchmarks show that this approach can reduce the initial test set size generated by TetraMAX18 while maintaining fault coverage.

Received: 01 April 2019      Published: 01 July 2020
Corresponding Authors: Liming Zhang