NORNS: Three Guides for Efficient Automatic Post-Fabrication Optimization of Modern NAND Flash Memory

In order to meet the diverse requirements of modern storage systems, flash memory should be optimized by precisely tuning a huge number of internal operating parameters. Although 3D NAND flash memory successfully increases the capacity of storage systems, its complex architecture and unique error behavior make such optimization a more difficult and time-consuming process during NAND manufacturing. This work introduces Norns, a novel method for post-fabrication optimization of NAND flash memory, which is an essential step in the manufacturing process of modern 3D NAND flash memory to simultaneously meet various requirements on reliability, performance, yield, etc. Norns is based on simple machine-learning approaches yet with three key guidelines that leverage (i) domain-specific rules, (ii) recent optimization results, and (iii) online simulation, respectively, to enable quick optimization of a large number of device parameters within the limited product turnaround time (TAT). We evaluate Norns in mass production for 7th-generation QLC NAND flash memory and 8th-generation TLC NAND flash memory. Our Norns can achieve superior optimization over existing post-fabrication optimization techniques by showing significant performance and reliability improvements by up to 8.8% and 12% on average, respectively.