A survey of repair analysis algorithms for memories

Current rapid advancements in deep submicron technologies have enabled the implementation of very large memory devices and embedded memories. However, the memory growth increases the number of defects, reducing the yield and reliability of such devices. Faulty cells are commonly repaired by using redundant cells, which are embedded in memory arrays by adding spare rows and columns. The repair process requires an efficient redundancy analysis (RA) algorithm. Spare architectures for the repair of faulty memory include one-dimensional (1D) spare architectures, two-dimensional (2D) spare architectures, and configurable spare architectures. Of these types, 2D spare architectures, which prepare extra rows and columns for repair, are popular because of their better repairing efficiency than 1D spare architectures and easier implementation than configurable spare architectures. However, because the complexity of the RA is NP-complete, the RA algorithm should consider various factors in order to determine a repair solution. The performance depends on three factors: Analysis time, repair rate, and area overhead. In this article, we survey RA algorithms for memory devices as well as built-in repair algorithms for improving these performance factors. Builtin redundancy analysis techniques for emergent three-dimensional integrated circuits are also discussed. Based on this analysis, we then discuss future research challenges for faulty-memory repair studies.