An Efficient Grouping Method for Large-Scale MBIST

Memory built-in self-test (MBIST) is an important design-for-test (DFT) technique for embedded memories, and MBIST grouping is the most significant part of it. However, as the number of embedded memories increases, previous MBIST grouping methods result in long grouping time or low-quality grouping results. In this paper, we propose an efficient grouping method for large-scale MBIST. Our method combines greedy algorithm (GA) with simulated annealing algorithm (SA), reducing grouping time significantly while maintaining high-quality grouping results. Experimental results show that, compared to previous SA-based method, our method can reduce grouping time by 43% for large-scale MBIST. It reduces the number of groups by 178 and 17 for large-scale MBIST compared to previous SA-based and G A-based methods, respectively.