Skhynix Inc.

Lsi Corporation

Avago Technologies General IP (singapore) Pte. Ltd.

Seagate Technology Incorporated

Xilinx, Inc.

Sk Hynix Memory Solutions Inc.

Samsung Electronics Co., Ltd.

Other

Lsi Logic Corporation

Honeywell International Inc.

Spiration, Inc. - Olympus Respiratory America

Broadcom International Pte. Ltd.

Fan Zhang

Aman Bhatia

Shaohua Yang

Yu Cai

Chenrong Xiong

Naveen Kumar

Meysam Asadi

Haobo Wang

Xuanxuan Lu

Jun Xiao

Shu Li

Yang Han

Zongwang Li

Wu Chang

June Lee

Ming Jin

Haitao Xia

Chung-Li Wang

Weijun Tan

David J Pignatelli

Hongwei Duan

Yi-Min Lin

Xuebin Wu

Abhiram Prabhakar

Anatoli Aleksandrovich Bolotov

Lei Chen

Lav D Ivanovic

Haibo Li

HyungSeok Kim

Abhiram Prabahkar

Erich Franz Haratsch

Kyoung Lae Cho

Kaitlyn T Nguyen

Jianqing Chen

Bruce Alexander Wilson

Johnson Yen

Mikhail I Grinchuk

Changyou Xu

Soo Jin Kim

Anup Kumar Sultania

Seyhan Karakulak

George Mathew

Qi Qi

Razmik Karabed

Brandon J Shuman

Lu Lu

David H Dillard

Ho-Ming Leung

Chun Zhang

Nicolas Guillaume Hostein

Madhusudan Kalluri

Peter A Lambe

Cassandra R Saleira

Randall D Mills

Yan Zhang

Chun Hok Ho

Yu Chin Fabian Lim

Qiuju Diao

ShiangJyh Steve Chang

Rui Cao

Lingqi Zeng

Yu Kou

Ivan Leonidovich Mazurenko

Xiangyu Tang

Frederick K H Lee

Kelly K Fitzpatrick

Xiufeng Song

Jongseung Park

Victor A Krachkovsky

Guenter Stenz

Bing Tian

Aleksey Alexandrovich Letunovskiy

Ivana Djurdjevic

Jefferson E Singleton

Paul G Filseth

Jianzhong Huang

Chaithanya Dudha

Shu Wang

Michael S Smith

Qi Zuo

Shyama P Mukherjee

Krishna Garlapati

Christine N Jurevicius

Ashutosh Verma

Abdel-Hakim S Alhussien

Suharli Tedja

Gary Chang

Joseph A Levert

Nithin Kumar Guggilla

Wern-Yan Koe

Daniel L Towery

Kevin G Christian

AbdelHakim S Alhussien

Abdel Hakim S Alhussien

Wei-Hao Yuan

Teodor Vlasov

Kasturiranga Rangam

Jaeyoung Jang

Taeil Um

Yequn Zhang

Devices, systems and methods for improving performance of a memory device are described. An example method includes obtaining a plurality of samples corresponding to a probability distribution for each of a plurality of cell voltage distributions of the memory device, each of the plurality of cell voltage distributions corresponding to a read voltage, determining, based on the samples obtained for the plurality of cell voltage distributions, a number of first deep neural networks (DNNs), estimating, for each of the first DNNs, one or more parameters of the corresponding probability distribution based on the plurality of samples, training, based on the samples and the corresponding one or more parameters, each of the first DNNs, and training, based on the samples and the one or more parameters from each of the first DNNs, a second DNN to enable generation of an updated read voltage value for retrieving information from the memory device.

Read threshold prediction in memory devices using deep neural networks

Devices, systems, and methods for improving performance of a non-volatile memory are described. An example method includes receiving, by the circuitry, a read command from a firmware. Herein, the firmware is configured to perform a media scan of the memory block, and the media scan comprises the read command. The method further includes the circuitry, in response to the read command, receiving raw data from the memory block, computing a metric associated with an accuracy of the raw data, and transmitting only the metric to the firmware. In this example, the circuitry comprises a checksum computation logic or an error correction code decoder, and the metric is a checksum or a failed bit count, respectively.

Health monitoring of a memory device using in-NAND processing

A memory device of a storage device may receive a garbage collection command from a memory controller coupled to the memory device to perform an internal garbage collection operation within the memory device. The memory device may attempt to perform the garbage collection operation to internally move valid memory pages from a first memory block to a second memory block to free up the first memory block in response to the garbage collection command. The memory device may send an acknowledgement to the memory controller for the garbage collection command.

In-memory garbage collection

A system and a method for searching for an optimal read threshold voltage. A memory device transfers to a controller only needed information (i.e., count formation) for each read, instead of transferring all the read data. The memory device includes: a pattern counter configured to: receive, from a memory cell, read data item, among multiple data items, corresponding to a read request; count, from the read data item, the number of cells reading as a particular value in multiple voltage level regions corresponding to multiple patterns for multiple pages among the plurality of pages, to generate count information; and provide the count information to the controller.

In-nand read threshold voltage search system and method

Devices, systems, and methods for improving performance of a memory device are described. An example method includes extracting parameters, which include a read threshold set, from each of a first set of host reads, replacing, based on the parameters, at least one host read from a second set of host reads by at least one host read from the first set of host reads, using a deep neural network (DNN) to generate an updated read threshold set, wherein an input to the DNN comprises the parameters from each of the second set of host reads subsequent to the replacing, and applying the updated read threshold set to the memory device to retrieve information from the memory device. In an example, the number of the first set of host reads is at least two orders of magnitude greater than the number of the second set of host reads.

Tracking read voltages in memory devices using deep neural networks

A memory device of a storage device may perform a test read of the memory device for each entry of a set of entries of an in-memory history read retry (HRR) table using a set of read thresholds corresponding to that entry. The memory device may identify an entry in the set of entries having a lowest failed bit count (FBC) obtained from the test read of each entry, and update the in-memory HRR table to have the identified entry with the lowest FBC be at a beginning of the in-memory HRR table. The memory device may also notify a memory controller coupled to the memory device to update an in-controller HRR table stored in the memory controller based on the update to the in-memory HRR table.

In-memory read threshold adaptation

Techniques for decoding a low-density parity check (LDPC) can include determining an asymmetric ratio of bit errors read as logic zero to bit errors read as logic one. The energy of each variable node of the LDPC codeword can be computed. For each variable node having an energy greater than a threshold energy, the variable node can be added to a collection of candidate bits for bit flipping. The asymmetric ratio can then be applied to flip bits in the collection of candidate bits to decode the LDPC codeword.

Error correction based on asymmetric ratio

A memory system and method for data reclaim which reads data from a memory using a preset voltage threshold based on a page type being read; records an initial set of program voltages for successful reads of the data from the memory; determines an initial distribution of the initial set of program voltages, over time subsequent sets of the program voltages for successful reads of the data from the memory; determines subsequent distributions of the subsequent sets of the program voltages; and based on program voltage variations of the subsequent distributions from the initial deviation, recycles the data in the memory.

PV distribution variation detection and data reclaim policy

A memory system having a memory block and a memory controller in communication with the memory block. The memory controller is configured to: decode codewords in the memory block; determine failed codewords based on one or more parity checks including a chipkill parity; and turbo-decode the failed codewords using at least two decoders in a feedback loop with adders prior to each decoder for scaling soft decode information prior to subsequent decoding of the failed codewords.

Data recovery with enhanced super chip-kill recovery and method of operating such memory system

A scheme for encoding and decoding a codeword into which address information is embedded. A write operation for this scheme includes generating tagging information including address information; encoding user information, meta information, the tagging information and additional shortened bits to generate parity information; generating a codeword including the user information, the meta information and the parity information; and storing the codeword in a memory device.

Address embedding for encoder and decoder and data processing method thereof

Devices, systems, and methods for improving performance of an iterative decoder in a non-volatile memory are described. An example method includes receiving a noisy codeword that is based on a transmitted codeword generated from an irregular low-density parity-check (LDPC) code, performing, in a first mode of the iterative decoder and on the noisy codeword for a number of iterations, a message passing algorithm between multiple variable nodes and multiple check nodes that represent a parity matrix of the irregular LDPC code, and determining, subsequent to performing the number of iterations, a candidate version of the transmitted codeword. In this first mode, and as part of performing the message passing algorithm, at least one check node of the multiple check nodes is configured to store a portion of multiple check node parameters instead of all of the multiple check node parameters.

Min-sum decoding for irregular low-density parity check codes in memory devices

A bit-flipping (BF) decoder and a decoding method based on a super node, which groups two or more component nodes corresponding to two or more bits in a codeword to generate a super node; and performs a decoding iteration on the super node. The decoding iteration includes: calculating a flipping energy for the super node based on a flipping energy for each of the component nodes and internal checks between the component nodes; and flipping at least one of the two or more bits in the super node upon a determination that the flipping energy for the super node exceeds a bit-flipping threshold.

Bit-flipping decoder and decoding method based on super node

Techniques for adjusting log likelihood ratios in a decoder may include determining an assist read (AR) zone based on performing assist reads (AR) of multibit memory cells of a memory. Soft reads of the multibit memory cells may be performed to determine a bin within the AR zone for each bit of the data stored in the memory cells. Each bin is associated with a log likelihood ratio (LLR) value. Error correction decoding on the data stored in the memory cells may be performed followed by collecting statistics on the decoded data for each bin in each AR zone. A hard error percentage may be computed for each AR zone based on the collected statistics, and one or more LLR values may be adjusted based on the hard error percentage.

Log likelihood ratios adjustment based on error tracker

Decoding method and memory system which group bits in irregular LDPC codes having similar degrees of convergence into respective degree groups, classify the degree groups according to a metric indicative of a number of decoding iterations for convergence, divide a time period for convergence of the decoding iterations into different zones for the processing of selected degree groups within each zone, and skip decoding of the bits in a non-converging zone where the bits are not converging.

Method and system for fast column processing for highly irregular LDPC codes

Devices, systems, and methods for improving performance of an iterative decoder in a non-volatile memory are described. An example method includes receiving a noisy codeword that is based on a transmitted codeword generated from a low-density parity-check (LDPC) code, performing, on the noisy codeword for a first number of iterations, a message passing algorithm between a plurality of variable nodes and a plurality of check nodes that represent a parity matrix of the LDPC code, assigning, based on comparing an asymmetric ratio to a threshold, a set of asymmetric LLRs to a set of LLRs (that were initialized using symmetric LLRs) used in the message passing algorithm, and determining, subsequent to performing the message passing algorithm on the noisy codeword for a second number of iterations, a candidate version of the transmitted codeword.

Machine-learning assisted decoder with asymmetric ratio tracking in non-volatile memory devices

Growing community of inventors

Fremont, CA, United States of America

Fan Zhang