Prediction of data retention degradation of a non-volatile memory device based on a machine learning algorithm

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.

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.