Evolutionary Optimization in Code-Based Test Compression

TL;DR

This paper introduces an evolutionary algorithm approach for code-based test compression, allowing flexible encoding of test sets with improved results over existing methods, especially for ISCAS circuits.

Contribution

It presents a novel evolutionary algorithm framework for code-based test compression that handles unspecified values, enhancing compression efficiency for complex test sets.

Findings

Improved compression ratios for ISCAS circuits.

Effective encoding of arbitrary test sets.

Outperforms existing techniques in experiments.

Abstract

We provide a general formulation for the code-based test compression problem with fixed-length input blocks and propose a solution approach based on Evolutionary Algorithms. In contrast to existing code-based methods, we allow unspecified values in matching vectors, which allows encoding of arbitrary test sets using a relatively small number of code-words. Experimental results for both stuck-at and path delay fault test sets for ISCAS circuits demonstrate an improvement compared to existing techniques.