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

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.