Health scan of a memory device using a code word error rate machine learning model

A method comprising:

• providing, by a processing device, updated assignments of a plurality of blocks from a plurality of dies to a plurality of voltage offset bins to a code word error rate (CWER) machine learning (ML) model;
• receiving, from the CWER ML model, a CWER distribution of a memory device;
• and
• receiving, from the CWER ML model, a CWER distribution of the memory device;
• and
• determining a tail slope value associated with the CWER distribution;
• determining whether the tail slope value satisfies a tail slope threshold criterion;
• and
• responsive to determining that the tail slope value associated with the CWER distribution satisfies the tail slope threshold criterion, causing data associated with a block family of a plurality of block families of the memory device to be refreshed, wherein the block family comprises a plurality of blocks residing within one or more dies of the plurality of dies.

A non-transitory computer-readable storage medium storing instructions, which when executed by a processing device coupled to a memory device of a memory sub-system, causes the processing device to perform operations comprising:

• providing updated assignments of a plurality of blocks from a plurality of dies to a plurality of voltage offset bins to a code word error rate (CWER) machine learning (ML) model;
• receiving, from the CWER ML model, a CWER distribution of the memory device;
• and
• determining a tail slope value associated with the CWER distribution;
• determining whether the tail slope value satisfies a tail slope threshold criterion;
• and
• responsive to determining that the tail slope value associated with the CWER distribution satisfies the tail slope threshold criterion, causing data associated with a block family of a plurality of block families of the memory device to be refreshed, wherein the block family comprises a plurality of blocks residing within one or more dies of the plurality of dies.