A Novel CNN-Based Redundancy Analysis Using Parallel Solution Decision

The increase in memory cell density and capacity has resulted in more faulty cells, necessitating the use of redundant memory row and column lines for repairs. However, existing redundancy analysis (RA) algorithms face a critical issue that RA time increases exponentially with the number of faulty cells. Furthermore, RA solutions for multiple memory chips cannot be derived simultaneously. In this study, a novel RA method is proposed using a convolutional neural network (CNN). The proposed RA algorithm also includes preprocessing to improve training accuracy. The solution locations on the fault map are predicted using multi-label classification. Moreover, parallel solution decision methods ensure that even if the CNN does not find the correct RA solution, an accurate final solution can still be derived, and PyCUDA is used to process multiple memories in parallel. From the experimental results, the normalized repair rate of the proposed RA is 100%. The RA time of the proposed RA is not affected by the number of faults but rather by the CNN execution time. Moreover, RA solutions for multiple memories can be quickly derived simultaneously by utilizing GPU parallel processing. In conclusion, a high yield and low test cost can be achieved.