Mathematical Yield Estimation for Two-Dimensional-Redundancy Memory Arrays

Abstract

Defect repair has become a necessary process to enhance the overall yield for memories since manufacturing a natural good memory is difficult in current memory technologies. This paper presents an yield-estimation scheme, which utilizes an induction-based approach to calculate the probability that all defects in a memory can be successfully repaired by a two-dimensional redundancy design. Unlike previous works, which rely on a time-consuming simulation to estimate the expected yield, our yield-estimation scheme only requires scalable mathematical computation and can achieve a high accuracy with limited time and space complexity. Also, the proposed estimation scheme can consider the impact of single defects, column defects, and row defects simultaneously. With the help of the proposed yield-estimation scheme, we can effectively identify the most profitable redundancy configuration for large memory designs within few seconds while it may take several hours or even days by using conventional simulation approach.