Western Digital Technologies, Inc.

Sandisk Technologies Inc.

David Rozman

Eran Sharon

Alon Eyal

Arthur Shulkin

Idan Alrod

Stella Achtenberg

Alexander Bazarsky

Dana Lee

Tomer Tzvi Eliash

Ariel Navon

Dudy David Avraham

Ishai Ilani

Alex Bazarsky

Deepanshu Dutta

Mark Murin

Changyuan Chen

Gadi Vishne

Evgeny Mekhanik

Tomer Eliash

Alexander Kalmanovich

Hadar Tagar

Rotem Feinblat

Wei-Cheng Lien

Yair Chasdai

A data storage device includes a memory device and a controller coupled to the memory device. When a read threshold calibration operation occurs, less than all of the pages of a representative wordline is sensed, such that read thresholds of less than all of the pages of the representative wordline is obtained. The obtained read thresholds and one or more physical conditions of the representative wordline are provided to a model to obtain the other read thresholds of the remaining pages of the representative wordline that were not sensed. The model correlates read thresholds of one page to another page of the same representative wordline and accounts for the one or more physical conditions of the representative wordline.

Light-weight BES approximation

Apparatuses, systems, and methods are presented for error correction based on physical characteristics for memory. A controller may be configured to read a set of encoded bits from a set of cells of a memory array. The controller may be configured to divide the encoded bits into reliability groups based on one or more persistent physical characteristics associated with cells of the set of cells. The controller may be configured to provide reliability estimates based on the reliability groups to a soft decision decoder for decoding the encoded bits.

Error correction based on physical characteristics for memory

Read reference levels are calibrated by calibrating integration times. An integration time is the length of time for which the charge on a sense node is allowed to change while the memory cell is being sensed. Calibrating the integration time is much faster than calibrating the reference voltage itself. This is due, in part, to reducing the number of different reference voltages that need to be applied during calibration. Calibrating the integration time may use different test integration times for a given read reference voltage, thereby reducing the number of read reference voltages. Hence, calibrating the integration time(s) is very efficient timewise. Also, power consumption may be reduced.

Read integration time calibration for non-volatile storage

A data storage device includes a non-volatile memory and a controller coupled to the non-volatile memory. The controller is operable to measure a first threshold voltage (VT) of a memory cell under a first parameter at a read temperature and measure a second VT of the memory cell under a second parameter at the read temperature in which the first parameter is different from the second parameter. A VT correction term for the memory cell is determined based upon the first VT measurement and the second VT measurement. A read VT of the memory cell is adjusted by using the VT correction term.

Temperature variation compensation

A method for finding a last good page in a memory system includes determining a first number of write operations in a first queue at a first page in a memory block of the memory system. The method also includes determining whether the first number of write operations in the first queue is above a threshold. The method also includes based on a determination that the first number of write operations in the first queue is above the threshold, determining whether a second page in the memory block is empty. The method also includes identifying, based on a determination that the second page is empty, the last good page in the memory block using a binary search between the first page and the second page.

Systems and methods for finding a last good page in NAND open block

Systems and methods are described for predicting an endurance of groups of memory cells within a memory device, based on current characteristics of the cells. The endurance may be predicted by processing historical information regarding operation of memory devices according to a machine learning algorithm, such as a neural network algorithm, to generate correlation information between characteristics of groups of memory calls at a first time and an endurance metric at a second time. The correlation information can be applied to current characteristics of a group of memory cells to predict a future endurance of that group. Operating parameters of a memory device may be modified at a per-block level based on predicted block endurances to increase the speed of a device, the longevity of a device, or both.

Identifying memory block write endurance using machine learning

Technology is disclosed herein for operating a non-volatile memory system following a heating event such as an Infrared (IR) reflow process. The memory system is pre-loaded with data that is stored at multiple bits per memory cell. After the heating event, the memory system calibrates the read reference levels for reading the memory cells at multiple bits per memory cell. However, prior to calibrating the read reference levels, the memory system stabilizes one or more conditions in the memory system, which allows the new read reference levels to be accurately calibrated. The memory system may stabilizes threshold voltage of memory cells and/or word line voltages, for example. In one aspect, a dummy read is performed to stabilize one or more conditions of the memory system. Hence, the read reference levels may be accurately calibrated.

Multi-level read after heating event in non-volatile storage

In a read scan operation, a first read level window is scanned for a first candidate read level that activates the fewest number of memory cells in relation to other candidate read levels within that window. A second read level window for a second candidate read level is then configured based on a correlation between at least one of the two adjacent memory states and one or more other adjacent memory states associated with the second read level window. The second read level window is scanned for a second candidate read level that activates the fewest number of memory cells, or results in the fewest bit errors, in relation to other candidate read levels within the second read level window. Next, a read operation is configured to use the first candidate read level and the second candidate read level.

Calibrating non-volatile memory read thresholds

Systems and methods are described for reducing error rates on data storage devices by applying data shaping to data written to such devices in order to avoid error-prone states on cells within the devices. Different states of individual cells (such as those representing different bit patterns) may have different propensities for error, and these propensities may vary during operation of a device. Thus, a device as disclosed herein may determine error-prone states for a cell or group of cells, and apply data shaping to data written to such cells to reduce the likelihood that writing the data places the cell or cells into an error-prone state. Data shaping may be used, for example, to increase the occurrence of '0' bits within input data, thus avoiding error-prone low voltage states that may be used to represent a series of '1' bits.

Data shaping to reduce error rates in solid state memory devices

A non-volatile storage apparatus includes a set of non-volatile memory cells and one or more control circuits in communication with the set of non-volatile memory cells. The one or more control circuits are configured to collect failure bit counts (FBCs) for data read from the set of non-volatile memory cells in a first time period and manage the set of non-volatile memory cells according to a probability of occurrence of a target FBC in a second time period that is subsequent to the first time period. The probability of occurrence of the target FBC during the second time period is calculated from a model of FBC distribution change of the set of non-volatile memory cells.

Non-volatile storage with wear-adjusted failure prediction

Disclosed is a system and method for adjusting read levels in a storage device based on bias functions. The method includes receiving a request to perform a memory access operation on a wordline of non-volatile memory. The method also includes selecting a bias function corresponding to the wordline of the non-volatile memory from a group of bias functions. The method also includes determining a bias value based on the selected bias function and the wordline. The method also includes adjusting a read level in the non-volatile memory based on the bias value. The method also includes performing the memory access operation on the wordline of the non-volatile memory using the adjusted read level. The bias functions may be linear functions and adjusted in response to detecting a recalibration condition.

System and method for adjusting read levels in a storage device based on bias functions

A data storage device includes a non-volatile memory and a controller coupled to the non-volatile memory. The controller is operable to measure a first threshold voltage (Vt) of a memory cell under a first parameter at a read temperature and measure a second Vt of the memory cell under a second parameter at the read temperature in which the first parameter is different from the second parameter. A Vt correction term for the memory cell is determined based upon the first Vt measurement and the second Vt measurement. A read Vt of the memory cell is adjusted by using the Vt correction term.

A device includes a memory and a controller coupled to the memory. The controller is configured to determine a temperature-based value of a search parameter in response to detecting that an error rate of a codeword read from the memory exceeds a threshold error rate. The controller is further configured to iteratively modify one or more memory access parameters associated with reducing temperature-dependent threshold voltage variation and to re-read the codeword using the modified one or more memory access parameters.

A non-volatile storage apparatus includes a set of non-volatile memory cells and one or more control circuits in communication with the set of non-volatile memory cells, the one or more control circuits are configured to collect failure bit counts (FBCs) for data read from the set of non-volatile memory cells, obtain one or more metrics of a cumulative distribution of the FBCs, calculate an indicator from the one or more metrics of the cumulative distribution of the FBCs and a target FBC, obtain a probability for the target FBC from the indicator, and manage at least one of: garbage collection, wear leveling, and read threshold voltage adjustment of the set of non-volatile memory cells according to the probability for the target FBC.

Non-volatile storage with failure prediction

A device includes a non-volatile memory and a controller coupled to the non-volatile memory. The controller is configured to initiate writing of first data to a first portion of a group of storage elements of the non-volatile memory. The controller is further configured to initiate writing of shaped dummy data to a second portion of the group of storage elements.

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Kiryat-Malakhi, Israel

David Rozman