A Scan Slice Reordering Algorithm Based on Minimizing Entropy to Enhance Test Data Compression Efficiency

To improve test data compression efficiency, the order of the scan unit need be adjusted, which indirectly changes the content of the test pattern. Scan chain partitioning is a common method that utilises this concept. However, current scan chain partitioning methods can still be optimised in terms of entropy and test data compression efficiency, and lack universality. To enhance the efficiency of code-based compression efficiency, we propose a scan slice reordering algorithm that minimizes entropy. This algorithm first calculates the rank of each scan slice or column vector of the test set, and then dynamically adjusts the order of the scan slices according to the descending order of these ranks in the pursuit of minimizing the entropy of the test set. By iterating through this process, the optimal position of each scan slice in the test set is ultimately determined. Compression experiments should be performed on all test patterns using different code-based schemes. This not only reduces the entropy of the test set but also significantly improves the efficiency of different codes. Compared to traditional scan chain partitioning method, FDR encoding achieved an average compression ratio increase of 6.16%, and RL-Huffman encoding achieved an average compression ratio increase of 4.96%. The experimental results demonstrate that our proposed algorithm is feasible, effective, and universal.