In the NAND flash manufacturing process, thousands of internal electronic fuses (eFuse) should be tuned in order to optimize performance and validity. In this paper, we propose a machine learning-based optimization technique that can automatically tune the individual eFuse value based on a deep learning and genetic algorithm. Using state-of-the-art triple-level cell (TLC) V-NAND flash wafers, we trained our model and validated its effectiveness. The experimental results show that our technique can automatically optimize NAND flash memory, thus reducing total turnaround time (TAT) by 70 % compared with the manual-based process.

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