Data Storage Device Calibrating Data Density via Inverted Signal Metrics
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Solution Overview
Problem
Existing data storage devices face limitations in optimizing data density and signal quality due to reliance on conventional quality metrics like AC-SNR, which may not fully address asymmetry and distortion in read signals.
Innovation Solution
The introduction of new quality metrics such as AC-SAR, AC-SDR, and AC-SNLDR, generated by amplitude-inverting and/or time-inverting signal samples, to provide a more comprehensive assessment of signal quality and optimize data storage device performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional quality metrics like AC-SNR are used to calibrate data density, then the calibration process is simple and fast, but the measurement precision is insufficient because it does not fully capture asymmetry and distortion in read signals
Solution Approach 1:
The patent segments the quality metric assessment into multiple independent components: AC-SNR for signal-to-noise ratio, AC-SAR for asymmetry, AC-SDR for distortion, and AC-SNLDR for non-linear distortion. Each component can be calculated separately and combined to provide comprehensive signal quality assessment, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent extends the traditional one-dimensional AC-SNR metric into multiple dimensions by adding asymmetry (AC-SAR), distortion (AC-SDR), and non-linear distortion (AC-SNLDR) components. This dimensional expansion allows comprehensive capture of signal quality characteristics without overwhelming complexity, as each dimension represents a specific aspect of signal degradation.
2Quantity of substance
If data density is increased to improve storage capacity, then more data can be stored per unit area, but signal quality deteriorates due to increased asymmetry and distortion in read signals
Solution Approach 1:
The patent implements a feedback mechanism where the calculated quality metrics (AC-SAR, AC-SDR, AC-SNLDR) are fed back into the calibration process to adjust data density settings. This allows real-time optimization of data density while maintaining signal quality, resolving the contradiction between storage capacity and reliability.
Solution Approach 2:
The patent changes the calibration parameters from simple AC-SNR to multiple quality metrics including asymmetry, distortion, and non-linear distortion parameters. These parameter changes enable precise control of data density calibration while accounting for signal quality degradation, allowing optimization of both storage capacity and reliability.
3Measurement precision
If amplitude-inverted and time-inverted signal samples are processed to generate additional quality metrics, then comprehensive assessment of signal quality is achieved, but computational complexity increases
Solution Approach 1:
The patent performs amplitude inversion and time inversion operations as preliminary actions during the signal sampling process. By preparing the inverted signal samples in advance and storing them alongside the original samples, the system avoids complex real-time processing while still achieving comprehensive quality assessment, thus reducing overall computational complexity.
Solution Approach 2:
The patent creates copies of the original signal samples through amplitude inversion and time inversion operations. These copies are then processed independently to generate additional quality metrics (AC-SAR, AC-SDR, AC-SNLDR). The copying approach allows parallel processing and simplifies the computational burden by breaking down the complex analysis into separate, manageable operations.
Data Source
AI summary
A data storage device is disclosed comprising a head actuated over a disk. A test pattern is read from a first part of the disk to generate a first read signal that is sampled to generate a first sequence of signal samples. The test pattern is read from a second part of the disk to generate a second read signal that is sampled to generate a second sequence of signal samples. A third sequence of signal samples is generated by at least one of amplitude-inverting the second sequence of signal samples, time-inverting the second sequence of signal samples, and amplitude-inverting and time-inverting the second sequence of signal samples. A quality metric is generated based on the first sequence of signal samples and the third sequence of signal samples, and a data density of the disk is configured based on the quality metric.