GRAP: Efficient GPU-Based Redundancy Analysis Using Parallel Evaluation for Cross Faults

Various memory repair methodologies based on redundancy analysis (RA) have been developed to improve the memory yield. However, conventional RAs often encounter difficulties in finding repair solutions for cases involving a large number of faults and redundancies. To address this problem, an efficient graphics processing unit (GPU)-based RA is proposed using Parallel evaluation for cross faults (GRAP). GRAP involves a preprocessing stage during memory testing, leveraging the parallel processing capacities of the GPU. Preprocessing facilitates rapid solution search by analyzing the fault information. After the test, the solution search is performed. The GPU threads are used to implement all possible cases of redundancy allocation, focusing on cross faults. The remaining faults are categorized by allocating the corresponding redundancies using an efficient method. Given that the solution search process efficiently exploits the multiple threads, GRAP can rapidly find a solution even in cases with a large number of faults and redundancies. Experiments are performed using the compute unified device architecture (CUDA) library for GPU parallel processing, and the performance of the GRAP is compared with those of conventional RA methodologies. The results demonstrated that the proposed RA method can achieve an optimal repair rate with a high analysis speed by leveraging efficient parallel computing.