US 11,854,629 B2
System and method for non-parametric optimal read threshold estimation using deep neural network
Fan Zhang, Fremont, CA (US); Aman Bhatia, Los Gatos, CA (US); and Haobo Wang, San Jose, CA (US)
Assigned to SK hynix Inc., Gyeonggi-do (KR)
Filed by SK hynix Inc., Gyeonggi-do (KR)
Filed on Nov. 22, 2021, as Appl. No. 17/532,905.
Prior Publication US 2023/0162803 A1, May 25, 2023
Int. Cl. G11C 16/26 (2006.01); G11C 16/34 (2006.01); G11C 16/10 (2006.01); G06N 3/063 (2023.01); G11C 16/14 (2006.01); G06F 18/214 (2023.01)
CPC G11C 16/3404 (2013.01) [G06F 18/214 (2023.01); G06N 3/063 (2013.01); G11C 16/102 (2013.01); G11C 16/14 (2013.01); G11C 16/26 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A memory system comprising:
a memory device including a plurality of pages; and
a controller including a neural network and configured to:
perform one or more read operations on a page selected from among the plurality of pages using a read threshold set including a plurality of read threshold voltages;
obtain the read threshold set, a checksum value and an asymmetric ratio of ones count and zeros count which are associated with decoding of the selected page according to each of the read operations;
provide the obtained read threshold set, the checksum value and the asymmetric ratio as input information to the neural network; and
estimate, by the neural network, an optimal read threshold voltage based on the input information and weights for the input information, the weights including a combination of multiple matrices and bias vectors.