TY - GEN
T1 - Norns
T2 - 43rd International Conference on Computer-Aided Design, ICCAD 2024
AU - Kim, Earl
AU - Cho, Hyunuk
AU - Cho, Sungjun
AU - Kim, Myungsuk
AU - Park, Jisung
AU - Jeong, Jaeyong
AU - Kim, Eunkyoung
AU - Hur, Sunghoi
N1 - Publisher Copyright:
© 2024 Copyright is held by the owner/author(s).
PY - 2025/4/9
Y1 - 2025/4/9
N2 - 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.
AB - 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.
KW - NAND flash memory
KW - evolutionary algorithm
KW - machine learning
KW - performance
KW - post-fabrication optimization
KW - reliability
UR - https://www.scopus.com/pages/publications/105003643917
U2 - 10.1145/3676536.3676825
DO - 10.1145/3676536.3676825
M3 - Conference contribution
AN - SCOPUS:105003643917
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 October 2024 through 31 October 2024
ER -