In the NAND flash manufacturing process, thousands of internal electronic fuses (eFuse) are tuned in order to optimize performance and validity. In this paper, we propose a machine learning optimization technique that uses deep learning (DL) and genetic algorithms (GA) to automatically tune eFuse values. Using state-of-the-art triple-level cell (TLC) V-NAND flash wafers, we trained our model and validated its effectiveness. Based on the findings of the evaluation and production data, the proposed optimization technique can reduce total turnaround time (TAT) by 70% compared with manual eFuse tuning.

We have adopted various defect detection systems in the front stage of manufacturing in order to effectively manage the quality of flash memory products. In this paper, we propose an intelligent pattern recognition methodology which enables us to discriminate abnormal wafer automatically in the course of NAND flash memory manufacturing. Our proposed technique consists of the two steps: pre-processing and hybrid clustering. The pre-processing step based on process primitives efficiently eliminates noisy data. Then, the hybrid clustering step dramatically reduces the total amount of computing, which makes our technique practical for the mass production of NAND flash memory.