Multi-Bank Optimized Redundancy Analysis Using Efficient Fault Collection

With technological advancements, the density and capacity of memory are rapidly increasing. As the number of memory cells increases, the difficulty of fault analysis and the number of faults also increase. Hence, the yield and test cost of memory have become essential issues in memory manufacturing. Many manufacturers have used redundancy analysis (RA) to improve the memory yield and decrease the test cost. However, most conventional RA methods require a lengthy analysis time to find a repair solution, and it is difficult to obtain an optimal repair rate with conventional RA algorithms. Although several algorithms using various spare structures to achieve performance improvement have been proposed, those improvements have not been groundbreaking. In this paper, a new multi-bank optimized redundancy analysis (MORA) algorithm is proposed. It achieves a very high repair rate and a drastic reduction in the analysis time compared with conventional RA algorithms using various spare structures. During testing, the proposed algorithm stores the faulty cell information efficiently. Therefore, the analysis time can be shortened through the pre-solution process of the repair analysis using the proposed fault storage spaces. Additionally, the proposed spare structures are used to increase the repair rate. The experimental results reveal that the proposed algorithm can achieve a very high repair rate at a faster speed than conventional RA algorithms.