US 12,260,877 B2 · Machine learning defect management in storage devices

Abstract

Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. Machine learning modeling is used to reconstruct the data for the untested cylinders. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges. In a test mode, some portion of the cylinders is chosen for testing. Each wedge in the chosen cylinders is tested and labeled defective or non-defective. The test data for each defective wedge is run through a machine learning defect management logic, and inferences are made for the defective/non-defective status of the untested wedges.

Claims23

Dependent claims

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.

10 more elements

Dependencies are inferred from the claim text.

Machine learning defect management in storage devices

Life of this patent

Machine learning defect management in storage devices

Life of this patent

Abstract

Claims23