The data compression book (2nd ed.)
The data compression book (2nd ed.)
Data and image compression (4th ed.): tools and techniques
Data and image compression (4th ed.): tools and techniques
Data compression via textual substitution
Journal of the ACM (JACM)
Universal Data Compression Based on the Burrows-Wheeler Transformation: Theory and Practice
IEEE Transactions on Computers
Data Compression in Digital Systems
Data Compression in Digital Systems
A Methodology to Design Efficient BIST Test Pattern Generators
Proceedings of the IEEE International Test Conference on Driving Down the Cost of Test
Test Width Compression for Built-In Self Testing
Proceedings of the IEEE International Test Conference
An Efficient Method for Compressing Test Data
Proceedings of the IEEE International Test Conference
4.1 COMPACT: A Hybrid Method for Compressing Test Data
VTS '98 Proceedings of the 16th IEEE VLSI Test Symposium
19.1 Built-In Self Testing of Sequential Circuits Using Precomputed Test Sets
VTS '98 Proceedings of the 16th IEEE VLSI Test Symposium
A Fast Algorithms for Making Suffix Arrays and for Burrows-Wheeler Transformation
DCC '98 Proceedings of the Conference on Data Compression
The Context Trees of Block Sorting Compression
DCC '98 Proceedings of the Conference on Data Compression
| Hi-index | 14.98 |
The overall throughput of automatic test equipment (ATE) is affected by the download time of test data. An effective approach to the reduction of the download time is to compress test data before the download. A compression algorithm for test data should meet two requirements: lossless and simple decompression. In this paper, we propose a new test data compression method that aims to fully utilize the unique characteristics of test data compression. The key idea of the proposed method is to perform the Burrows-Wheeler transformation on the sequence of test patterns and then to apply run-length coding. Experimental results show that our compression method performs better than six other methods for compressing test data. The average compression ratio of the proposed method performed on 15 test data sets is 94.6, while that for the next best one, Gzip, is 65.0. The experimental results also show that our method indeed reduces the download time significantly, provided a dedicated hardware decompressor is employed.