A new low energy BIST using a statistical code
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Scan-chain partition for high test-data compressibility and low shift power under routing constraint
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A novel x-ploiting strategy for improving performance of test data compression
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Proceedings of the Conference on Design, Automation and Test in Europe
Compression-aware capture power reduction for at-speed testing
Proceedings of the 16th Asia and South Pacific Design Automation Conference
An Efficient Block Entropy Based Compression Scheme for Systems-on-a-Chip Test Data
Journal of Signal Processing Systems
Boolean functions over nano-fabrics: improving resilience through coding
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
| Hi-index | 0.03 |
The entropy of a set of data is a measure of the amount of information contained in it. Entropy calculations for fully specified data have been used to get a theoretical bound on how much that data can be compressed. This paper extends the concept of entropy for incompletely specified test data (i.e., that has unspecified or don't care bits) and explores the use of entropy to show how bounds on the maximum amount of compression for a particular symbol partitioning can be calculated. The impact of different ways of partitioning the test data into symbols on entropy is studied. For a class of partitions that use fixed-length symbols, a greedy algorithm for specifying the don't cares to reduce entropy is described. It is shown to be equivalent to the minimum entropy set cover problem and thus is within an additive constant error with respect to the minimum entropy possible among all ways of specifying the don't cares. A polynomial time algorithm that can be used to approximate the calculation of entropy is described. Different test data compression techniques proposed in the literature are analyzed with respect to the entropy bounds. The limitations and advantages of certain types of test data encoding strategies are studied using entropy theory