A Clustering-based ML Scheme for Capacity Approaching Soft Level Sensing in 3D TLC NAND

Li-Wei Liu; Yen-Chin Liao; Hsie-Chia Chang

ICASSP 2022accepted0 citations

A Clustering-based ML Scheme for Capacity Approaching Soft Level Sensing in 3D TLC NAND

Li-Wei Liu, Yen-Chin Liao, Hsie-Chia Chang

Abstract

In a 3D TLC solid-state storage system, the LDPC decoding performance is significantly affected by the quality of soft-level sensing. Inspired by the capacity-approaching maximum mutual-information method, this work presents the data-driven approach to collect all the optimal 2-bit soft-read level pairs over the 3D TLC NAND. Due to the data transmission latency and limited configuration resources, a clustering method is proposed to extract the soft-read level pairs in the experiment data. Under the 3K Program Erase Cycles 228-hour data retention at 85°C channel condition, the proposed soft-read level pairs could provide an additional 73-error-bit tolerance in the 2K LDPC decoder.

BibTeX

@inproceedings{icassp2022_aclusteringbased,
  title = {A Clustering-based ML Scheme for Capacity Approaching Soft Level Sensing in 3D TLC NAND},
  author = {Li-Wei Liu and Yen-Chin Liao and Hsie-Chia Chang},
  booktitle = {ICASSP 2022},
  year = {2022}
}