Hard Disk Drive Read Channel Calibration Using User Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Typical hard disk drive read channel adaptation and training techniques often result in inaccurate and sub-optimal calibration due to the use of data patterns.
Innovation Solution
Utilizing user data for read channel training and adaptation by activating the disk write logic and write channel encoding logic during disk read, allowing for precise calibration by comparing encoded data from auxiliary memory with decoded data from the hard disk drive, and synchronizing and formatting outputs for error determination and parameter tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data patterns are used for read channel training and adaptation, then the training process can be performed, but the calibration accuracy becomes insufficient
Solution Approach 1:
The patent uses user data that has been previously written to the hard disk drive as a copy of actual operational data. This copied data is then used for read channel training and adaptation, replacing traditional synthetic data patterns. The user data accurately represents real data characteristics, thereby improving calibration accuracy and training effectiveness simultaneously.
2Manufacturing precision
If traditional data patterns are used for calibration, then the training process is simpler, but the calibration results are sub-optimal
Solution Approach 1:
The system uses its own previously written user data as the training dataset, eliminating the need for external test patterns or calibration sequences. The hard disk drive performs self-calibration using data that it has already processed and stored, thereby achieving high calibration precision without significantly increasing process complexity.
Solution Approach 2:
The user data is captured and stored in advance during normal write operations. This preliminary capture of actual operational data allows the calibration process to use authentic data characteristics without requiring complex real-time data generation or external testing equipment.
Data Source
AI summary
Calibrating a read channel is disclosed. Previously written user data is read from an auxiliary memory. The previously written user data is processed through a plurality of write channel stages. The output of at least one of the plurality of write channel stages is compared to the output of a corresponding read channel stage to generate an error signal.