STORAGE SYSTEM WITH MACHINE LEARNING MECHANISM AND METHOD OF OPERATION THEREOF

1 . A storage system comprising:

a control processor, configured to:

read user data,

calculate error statistics from the user data, and

operate a machine learning mechanism configured to identify a bad sector based on the error statistics; and

a non-volatile memory array, coupled to the control processor, configured to store the user data; and

wherein the control processor is further configured to map out the bad sector, based on the machine learning mechanism, and move the user data to a target sector for enhancing performance of the non-volatile memory array.

2 . The system as claimed in claim 1 wherein the control processor is further configured to monitor the error statistics for each of the sector 0 through sector N.

3 . The system as claimed in claim 1 wherein the control processor is further configured to refine the machine learning mechanism for determining a bad sector by monitoring the error statistics.

4 . The system as claimed in claim 1 wherein the control processor is further configured to operate a program/erase (P/E) interval monitor to pass the error statistics to the machine learning mechanism at selected intervals of the P/E cycle.

5 . The system as claimed in claim 1 wherein the control processor is further configured to calculate a non-linear component of the error statistics with past error statistics.

6 . The system as claimed in claim 1 wherein the control processor is further configured to operate the machine learning mechanism by calculating a bad sector indicator.

7 . The system as claimed in claim 1 wherein the control processor is configured to identify the bad sector includes comparing the error statistics to a bad sector threshold.

8 . The system as claimed in claim 1 wherein the control processor is further configured to predict the bad sector includes calculating a non-linear component of the error statistic.

9 . The system as claimed in claim 1 wherein the control processor is further configured to refine the machine learning mechanism when an uncorrectable error trigger is activated.

10 . The system as claimed in claim 1 wherein the control processor is further configured to restore the machine learning mechanism to an initial state.

11 . A method of operation of a storage system comprising:

reading user data from a non-volatile memory array;

calculating error statistics from the user data;

operating a machine learning mechanism with the error statistics;

identifying a bad sector by the machine learning mechanism; and

mapping out the bad sector including moving the user data to a target sector for enhancing performance of the non-volatile memory array.

12 . The method as claimed in claim 11 wherein reading the user data includes monitoring the bit error count for each of the sector 0 through sector N.

13 . The method as claimed in claim 11 further comprising refining the machine learning mechanism for determining a bad sector by monitoring the bit error count.

14 . The method as claimed in claim 11 further comprising passing the error statistics to the machine learning mechanism at selected intervals of the P/E cycle.

15 . The method as claimed in claim 11 further comprising calculating a non-linear component of the error statistics with past error statistics.

16 . The method as claimed in claim 11 wherein operating the machine learning mechanism with the error statistics includes calculating a bad sector indicator.

17 . The method as claimed in claim 11 wherein identifying the bad sector includes comparing the bit error count to a bad sector threshold.

18 . The method as claimed in claim 11 further comprising calculating a non-linear component of the error statistic.

19 . The method as claimed in claim 11 further comprising refining the machine learning mechanism when an uncorrectable error trigger is activated.

20 . The method as claimed in claim 11 further comprising restoring the machine learning mechanism to an initial state.

21 . A non-transitory computer readable medium including instructions for execution, the medium comprising:

reading user data from a non-volatile memory array;

calculating error statistics for the user data;

operating a machine learning mechanism with the error statistics;

identifying a bad sector by the machine learning mechanism; and

mapping out the bad sector including moving the user data to a target sector for enhancing performance of the non-volatile memory array.

22 . The medium as claimed in claim 21 wherein reading the user data includes monitoring the bit error count for each of the sector 0 through sector N.

23 . The medium as claimed in claim 21 further comprising refining the machine learning mechanism for determining a bad sector by monitoring the bit error count.

24 . The medium as claimed in claim 21 further comprising passing the error statistics to the machine learning mechanism at selected intervals of the P/E cycle.

25 . The medium as claimed in claim 21 further comprising calculating a non-linear component of the error statistics with past error statistics.

26 . The medium as claimed in claim 21 wherein operating the machine learning mechanism with the error statistics includes calculating a bad sector indicator.

27 . The medium as claimed in claim 21 wherein identifying the bad sector includes comparing the bit error count to a bad sector threshold.

28 . The medium as claimed in claim 21 further comprising calculating a non-linear component of the error statistic.

29 . The medium as claimed in claim 21 further comprising refining the machine learning mechanism when an uncorrectable error trigger is activated.

30 . The medium as claimed in claim 21 further comprising restoring the machine learning mechanism to an initial state.