# US 2025/0165148 A1 · Prediction of data retention degradation of a non-volatile memory device based on a machine learning algorithm > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0165148 A1 - Document ID: US20250165148A1 - Status: Published application - Kind: A1 - Application number: 18/622,866 - Filed: 2024-03-29 - Published: 2025-05-22 - Earliest claimed: 2023-11-16 - Assignees: Microchip Technology Incorporated - Applicants: Microchip Technology Incorporated - Inventors: Pitamber SHUKLA; Chris NORRIE; Igor ZIPER - Web page: https://patentgrep.com/patent/US20250165148A1 ## CPC - G06F 3/0611: {in relation to response time} - G06F 3/0659: {Command handling arrangements, e.g. command buffers, queues, command scheduling} - G06F 3/0679: {Non-volatile semiconductor memory device, e.g. flash memory, one time programmable memory [OTP]} - G11C 11/5642: {Sensing or reading circuits; Data output circuits} - G11C 16/0483: {comprising cells having several storage transistors connected in series} - G11C 16/26: Sensing or reading circuits; Data output circuits - G11C 16/3418: {Disturbance prevention or evaluation; Refreshing of disturbed memory data} - G11C 16/349: {Arrangements for evaluating degradation, retention or wearout, e.g. by counting erase cycles} - G11C 29/021: {in voltage or current generators} - G11C 29/028: {with adaption or trimming of parameters} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G11C: STATIC STORES (semiconductor memory devices H10B) ## Abstract A controller, of a solid state drive (SSD), may perform, on one or more blocks of a non-volatile memory device of the SSD, read operations using pre-determined threshold voltages associated with two overlapped charge states. The read operations may be performed after a power-on condition following a power-off condition on the non-volatile memory device. The controller may determine, using a machine learning model, a change in threshold voltages associated with the two overlapped charge states, after the power-off condition. The machine learning model may determine the change in threshold voltages using bit error rates associated with the read operations. The machine learning model may be trained to determine changes in threshold voltages for the two overlapped charge states, after power-off conditions. The controller may determine adjusted threshold voltages associated with the two overlapped charge states based on the change in threshold voltages. ## Claims ### Claim 1 A method performed by a controller of a solid state drive (SSD), the method comprising: performing, on one or more blocks of a non-volatile memory device of the SSD, read operations using pre-determined threshold voltages associated with two overlapped charge states, wherein the read operations are performed after a power-on condition following a power-off condition on the non-volatile memory device; determining, using a machine learning model, a change in threshold voltages associated with the two overlapped charge states, after the power-off condition, wherein the machine learning model determines the change in threshold voltages using bit error rates associated with the read operations, wherein the machine learning model is trained using characterization data that identifies different changes in threshold voltages for different data retention conditions for the two overlapped charge states of one or more non-volatile memory devices, and wherein the machine learning model is trained to determine changes in threshold voltages for the two overlapped charge states, after power-off conditions; and determining adjusted threshold voltages associated with the two overlapped charge states based on the change in threshold voltages, wherein subsequent read operations are performed on the one or more blocks using the adjusted threshold voltages. ### Claim 2 (depends on 1) The method of claim 1, wherein the characterization data identifies different program/erase cycles associated with the different data retention conditions, wherein the machine learning model comprises a plurality of neural network models associated with the different program/erase cycles, and wherein the method comprises: selecting a neural network model, of the plurality of neural network models, based on a number of program/erase cycles of the one or more blocks; and determining, using the selected neural network model, the changes in threshold voltage associated with the two overlapped charge states after the power-off condition. ### Claim 3 (depends on 1) The method of claim 1, comprising: analyzing program/erase information identifying dates and times of program/erase cycles of a plurality of blocks of the non-volatile memory device; and identifying the one or more blocks as blocks, of the plurality of blocks, with most recent program/erase cycles based on analyzing the program/erase information. ### Claim 4 (depends on 1) The method of claim 1, comprising: performing program/erase cycles on the one or more blocks prior to the power-off condition; storing, prior to the power-off condition, program/erase information indicating that the program/erase cycles were performed on the one or more blocks; obtaining the program/erase information after the power-off condition; and identifying the one or more blocks based on the program/erase information. ### Claim 5 (depends on 1) The method of claim 1, comprising: performing, for a block of memory of the non-volatile memory device, additional read operations to determine threshold voltages associated with the two overlapped charge states; determining a health of the block based on the additional read operations and the change in threshold voltages associated with the two overlapped charge states; and determine, based on the health of the block, a period of time for performing a refresh operation on the block. ### Claim 6 (depends on 1) The method of claim 1, comprising: performing a lookup operation on a data structure, based on the change in threshold voltages, to determine threshold voltages of other charge states after the power-off condition. ### Claim 7 (depends on 1) The method of claim 1, comprising: performing a mathematical operation, using the change in threshold voltages, to determine threshold voltages of other charge states after the power-off condition. ### Claim 8 (depends on 1) The method of claim 1, wherein the power-off condition causes an unexpected loss of power on the non-volatile memory device, and wherein performing the read operations comprises: performing, for the one or more blocks, the read operations to determine threshold voltages included in an overlap between the two overlapped charge states, wherein the two overlapped charge states are associated with highest threshold voltages. ### Claim 9 A system comprising: a controller to: detect read errors based on read operations performed using pre-determined threshold voltages associated with two overlapped charge states, wherein the read operations are performed, on a non-volatile memory device, after a power-on condition following a power-off condition on the non-volatile memory device; determine, using a machine learning model, a change in threshold voltages associated with the two overlapped charge states after the power-off condition, wherein the change in threshold voltages is determined based on detecting the read errors, wherein the machine learning model is trained using characterization data that identifies different changes in threshold voltages for different data retention conditions for the two overlapped charge states of one or more non-volatile memory devices, and wherein the machine learning model is trained to determine changes in threshold voltages for the two overlapped charge states after power-on conditions following power-off conditions; and adjust, based on the change in threshold voltages, the pre-determined threshold voltages to obtain adjusted threshold voltages, wherein subsequent read operations are performed on the non-volatile memory device using the adjusted threshold voltages. ### Claim 10 (depends on 9) The system of claim 9, wherein the machine learning model comprises a neural network model. ### Claim 11 (depends on 9) The system of claim 9, wherein the controller is to: perform a lookup operation of a data structure, based on the change in threshold voltages, to determine threshold voltages of other charge states after the power-off condition. ### Claim 12 (depends on 9) The system of claim 9, wherein the controller is to: perform a mathematical operation, using the change in threshold voltages, to determine threshold voltages of other charge states after the power-off condition. ### Claim 13 (depends on 9) The system of claim 9, wherein the controller is to: perform, for a block of the non-volatile memory device, additional read operations to determine threshold voltages associated with the two overlapped charge states; determine a health of the block based on the additional read operations and the change in threshold voltages associated with the two overlapped charge states; and determine, based on the health of the block, that a refresh operation is to be performed on the block. ### Claim 14 (depends on 9) The system of claim 9, wherein the controller is to: analyze program/erase information identifying dates and times of program/erase cycles of a plurality of blocks of the non-volatile memory device; and identify one or more blocks as blocks with most recent program/erase cycles based on analyzing the program/erase information. ### Claim 15 A computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: program instructions to detect read errors based on read operations performed using threshold voltages associated with two overlapped charge states, wherein the read operations are performed after a power-on condition following a power-off condition on a non-volatile memory device, and wherein the read operations are performed on one or more blocks of the non-volatile memory device; and program instructions to determine, using a machine learning model, a change in threshold voltages associated with the two overlapped charge states after the power-off condition, wherein the machine learning model is trained using characterization data that identifies different changes in threshold voltages for different data retention conditions for the two overlapped charge states of one or more non-volatile memory devices, wherein the machine learning model is trained to determine changes in threshold voltages for the two overlapped charge states, after power-off conditions, and wherein subsequent read operations are performed on the one or more blocks based on the change in threshold voltages. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the program instructions comprise: program instructions to analyze program/erase information identifying dates and times of program/erase cycles of a plurality of blocks of the non-volatile memory device; and program instructions to identify the one or more blocks as blocks, of the plurality of blocks, with most recent program/erase cycles based on analyzing the program/erase information. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the program instructions comprise: program instructions to perform, for a block of the non-volatile memory device, additional read operations to determine threshold voltages associated with the two overlapped charge states; program instructions to determine a health of the block based on the additional read operations and the change in threshold voltages associated with the two overlapped charge states; and program instructions to determine, based on the health of the block, that a refresh operation is to be performed on the block. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein the program instructions comprise: program instructions to perform a mathematical operation, using the change in threshold voltages, to determine threshold voltages of other charge states after the power-off condition. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the program instructions comprise: program instructions to a lookup operation of a data structure, based on the change in threshold voltages, to determine threshold voltages of other charge states after the power-off condition. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein the machine learning model comprises a neural network model.