Machine Learning Read Offset Estimation for Hard Disk Drive Heads
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Solution Overview
Problem
Existing rule-based systems for estimating read offset in hard disk drives are susceptible to errors due to track squeeze and poor signal quality, particularly in narrower tracks with increasing areal density, leading to mistracking and increased failure in reading recorded data.
Innovation Solution
A machine learning approach using neural networks is employed to estimate read offset by processing components extracted from user data, such as Volterra coefficients and MISO filter values, which can handle nuisance parameters and provide more accurate radial position adjustments during read operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based systems are used for read offset estimation, then the system complexity is low, but the measurement precision deteriorates due to track squeeze and poor signal quality
Solution Approach 1:
The patent replaces rule-based estimation systems with machine learning-based systems that use neural networks to estimate read offset. The machine learning model processes multiple input features (servo sector data, user data, channel equalization parameters) to predict read offset, replacing traditional mechanical/rule-based estimation methods with intelligent algorithms that adapt to varying track conditions and signal quality.
Solution Approach 2:
The patent combines multiple data sources and features into a composite input for the machine learning model, including servo sector positions, user data characteristics, channel equalization parameters, and read signal quality metrics. This composite approach integrates diverse information streams to improve estimation accuracy under varying conditions.
2Productivity
If track width is reduced to increase areal density, then the productivity increases, but the reliability deteriorates due to increased susceptibility to track squeeze and mistracking
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously estimates read offset during data reading operations and uses this information to adjust the radial position of the read head via actuators. This closed-loop feedback system compensates for track squeeze and mistracking in real-time, maintaining reliable data reading despite reduced track width.
Solution Approach 2:
The patent dynamically adjusts read head position parameters based on machine learning predictions of read offset. The system changes radial position parameters in response to detected track conditions, adapting to track squeeze and signal quality variations that occur with narrower tracks at higher areal densities.
3Device complexity
If traditional read offset estimation methods are used, then the device complexity is low, but the measurement precision deteriorates in the presence of nuisance parameters
Solution Approach 1:
The patent replaces traditional rule-based estimation devices with machine learning-based estimation systems. The neural network processes multiple input features including servo data, user data characteristics, and channel parameters to predict read offset, substituting simple rule-based devices with intelligent systems that robustly handle nuisance parameters like track squeeze and signal degradation.
Data Source
AI summary
Components are extracted from user data being read from a reader of a hard disk drive. The components collectively indicate both a magnitude and direction of a read offset of the reader over a track. The components are input to a machine-learning processor during operation of the hard disk drive, causing the machine-learning processor to produce an output. A read offset of the reader is estimated during the operation of the hard drive head based on the output of the machine learning processor. While reading the user data, a radial position of the reader over the track is adjusted via an actuator based on the estimated read offset.