US 12,260,877 B2
Machine learning defect management in storage devices
Saket Giri, San Jose, CA (US); Anand Lallan Gupta, San Jose, CA (US); Jonathan Lloyd, Newport Beach, CA (US); and Amit Chattopadhyay, San Jose, CA (US)
Assigned to Western Digital Technologies, Inc., San Jose, CA (US)
Filed by Western Digital Technologies, Inc., San Jose, CA (US)
Filed on Aug. 14, 2023, as Appl. No. 18/449,278.
Claims priority of provisional application 63/482,232, filed on Jan. 30, 2023.
Prior Publication US 2024/0257835 A1, Aug. 1, 2024
Int. Cl. G11B 19/04 (2006.01); G11B 5/596 (2006.01)
CPC G11B 19/048 (2013.01) [G11B 5/59633 (2013.01); G11B 2220/2516 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A device, comprising:
a rotating disk;
a head actuated over the disk;
a plurality of concentric data tracks disposed on the disk, wherein:
the tracks are grouped into cylinders,
the tracks are partitioned into radially-adjacent sectors, and
radially-adjacent sectors in the cylinders are grouped into wedges; and
control circuitry comprising a Machine Learning Defect Management (MLDM) logic, wherein the MLDM logic is configured to:
select a portion of the cylinders for testing;
test the wedges in the selected cylinders for defects;
input the results of the detected defective wedges to one or more machine learning models; and
use the one or more machine learning models to infer the defective or non-defective status of the wedges in untested cylinders.