Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Tomer Tzvi Eliash

Alexander Bazarsky

Yuval Grossman

Eran Sharon

Arthur Shulkin

Ran Zamir

Karin Inbar

David Rozman

Ofir Pele

Rami Rom

Asaf Gueta

Inon Cohen

Shemmer Choresh

Yehuda Hahn

Ariel Navon

Shay Benisty

Huai-Yuan Tseng

Yu-Chung Lien

Dudy David Avraham

Daniel Joseph Linnen

Kirubakaran Periyannan

Michael Micha Ionin

Mahim Raj Gupta

Nika Yanuka

Lola Grin

Itay Busnach

Lior Avital

Lior Bublil

James Tom

James Yin Tom

Jonathan Sokolowski

Ofer Privman

Roy Peretz

Exemplary methods and apparatus are provided for implementing a deep learning accelerator (DLA) or other neural network components within the die of a non-volatile memory (NVM) apparatus using, for example, under-the-array circuit components within the die. Some aspects disclosed herein relate to configuring the under-the-array components to implement feedforward DLA operations. Other aspects relate to backpropagation operations. Still other aspects relate to using an NAND-based on-chip copy with update function to facilitate updating synaptic weights of a neural network stored on a die. Other aspects disclosed herein relate to configuring a solid state device (SSD) controller for use with the NVM. In some aspects, the SSD controller includes flash translation layer (FTL) tables configured specifically for use with neural network data stored in the NVM.

Non-volatile memory die with deep learning neural network

For bit errors caused by intrinsic cell variations, the bit errors are scattered across a page of memory. However, for bit errors caused by a physical issue in memory, the bit errors cluster together within the same memory area. In an example data storage device, a page of memory is divided into sections, and counters are used to count the number of errors in each section. A physical error location is detected if the number exceeds a parameter, and as compared to the number of errors in the other sections. In another example data storage device having an error correction code (ECC) engine, a histogram and binomial probability are used to detect physical errors. This has the advantage of detecting weak memory blocks that are about to fail, so the blocks can be retired early as a grown bad block.

Data storage device and method for identifying a failing area of memory based on a cluster of bit errors

Apparatus, methods, media and systems for multiple sets of trim parameters are described. A non-volatile memory device may comprise a first register, a second register, a multiplexer, a first set of I/O lines, each coupled to the first register and the multiplexer, each associated with a particular trim set among multiple trim sets stored in the first register, one or more second I/O lines, each coupled to the second register and the multiplexer. The multiplexer is configured to receive a control signal. The multiplexer is configured to output, based on the control signal, a particular trim set among the multiple trim sets to the second register using the one or more second I/O lines.

Multiple sets of trim parameters

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to determine a read threshold on a wordline, adjust a read threshold voltage level associated with the read threshold, determine an adjusted read threshold at the adjusted read threshold voltage level, where the adjusted read threshold is different from the read threshold, compare the adjusted read threshold to the read threshold, and calibrate the read threshold based on the comparing. The controller is further configured to analyze a bit error rate (BER) difference based on the calibrating and/or a previous read threshold voltage level movement, choose a next target read threshold for next calibration, and read a second page at the next target read threshold.

Read level tracking by random threshold movement with short feedback loop

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to determine that a power loss event has occurred, determine that one or more blocks are in an erased state, examine a block of the one or more blocks to determine whether the block is a SLC erased block or a TLC erased block, and place the block in a SLC pre-erase heap if the block is the SLC erased block or in a TLC pre-erase heap if the block is the TLC erased block. The controller is further configured to determine a first bit count of page0 for a SLC voltage for the block, determine a second bit count of page1 for a TLC voltage for the block, and classify the block as either a SLC erased block or a TLC erased block.

Block erase type detection using bit count check

Disclosed are systems and methods for efficient power management for storage devices. A method includes receiving a request to transition a flash memory to a first stand-by mode, wherein the flash memory comprises a plurality of dies. The method also includes causing one or more guard dies of the plurality of dies to transition to the first stand-by mode while causing one or more other dies of the plurality of dies to transition to a second stand-by mode, wherein the second stand-by mode is configured to consume less power than the first stand-by mode. The method also includes receiving an input/output (I/O) request for the flash memory. The method also includes causing the I/O request to be performed on the one or more guard dies that are in the first stand-by mode but not in the second stand-by mode.

Efficient power management modes for multiple memory devices

Systems and methods for storing data are described. A system can comprise a controller, one or more physical non-volatile memory devices, a bus comprising a plurality of input/output (I/O) lines. The controller configured to receive data, encode the received data into a codeword, and transfer, in parallel, different portions of the codeword to different physical non-volatile memory devices among the plurality of physical non-volatile memory devices.

Data storage system for improving data throughput and decode capabilities

An integrated memory assembly comprises a memory die and a control die bonded to the memory die. The memory die includes a memory structure of non-volatile memory cells. The control die is configured to program user data to and read user data from the memory die based on one or more operational parameters. The control die is configured to calibrate the one or more operational parameters for the memory die. The control die is also configured to perform testing of the memory die using the calibrated one or more operational parameters.

Calibration for integrated memory assembly

Apparatus, media, methods, and systems for data storage systems and methods for optimized scheduling of background management operations. A data storage system may comprise a controller. The controller is configured to determine a timeout value of an adaptive timeout parameter of the data storage system. The controller is configured to determine whether a first host operation is received. The controller is configured to, when the first host operation is not received, determine whether the timeout value satisfies a threshold value. The controller is configured to, when the timeout value satisfies the threshold value, cause one or more background management operations to be executed at the data storage system.

Data storage systems and methods for optimized scheduling of background management operations

A storage device includes a controller that can dynamically adjust the zone active limit (ZAL) for a zoned namespace (ZNS). Rather than assuming a worst-case scenario for the ZNS, the ZAL can be dynamically adjusted, even after providing the ZAL to a host device. In so doing, device behavior changes due to factors such as temperature, failed or flipped bit count, and device cycling can be considered as impacting the ZAL. The ZAL can then be adjusted over time, and the new ZAL can be communicated to the host device. As such, rather than a fixed, worst-case ZAL, the host device will receive updated ZAL values over time as the device performs.

Dynamic ZNS open zone active limit

Enhanced read-ahead capabilities for storage devices are disclosed herein. In an implementation, data for a given write operation may be written to storage such that the location of the next write is stored with the data. Later, when the data is being read from storage, other data may be pre-fetched from the location of the next write that was written with the data. If the next location is the target of a subsequent read operation, the other data may be returned to the host immediately, since it will have already been read from the location where it was stored, thereby speeding-up the subsequent read operation.

Enhanced read-ahead capability for storage devices

Growing community of inventors

Kfar Saba, Israel

Tomer Tzvi Eliash