Storage Controller Data Quality Metric Error Recovery
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Solution Overview
Problem
Existing storage devices face challenges in reducing the time required for error correction and minimizing power consumption, especially when dealing with poor data quality.
Innovation Solution
The implementation of a data quality metric in storage devices allows for the estimation of data quality based on the number of violated check equations, enabling the selection of an appropriate error recovery scheme to reduce latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error correction is applied to ensure data reliability, then data reliability is improved, but response time increases
Solution Approach 1:
The storage device performs preliminary assessment of data quality metrics (such as read disturbance, program/erase cycle counts, and error patterns) before executing error correction. This allows the system to pre-determine whether error correction is necessary, avoiding unnecessary correction operations and reducing response time for data with acceptable quality levels.
Solution Approach 2:
The system dynamically adjusts error correction parameters based on data quality metrics. When quality metrics indicate poor data condition, more aggressive error correction is applied; when metrics show good quality, minimal or no correction is performed. This adaptive parameter adjustment optimizes the balance between reliability and response time.
2Reliability
If error correction techniques are applied to improve data reliability, then data reliability is improved, but power consumption increases
Solution Approach 1:
The storage device performs preliminary assessment of data quality metrics before executing error correction, allowing it to pre-determine whether correction is necessary. This prevents unnecessary power consumption on data that does not require correction while maintaining reliability for degraded data.
Solution Approach 2:
The system dynamically adjusts error correction intensity based on data quality metrics, applying stronger correction only when needed. This selective approach reduces overall power consumption compared to always applying maximum error correction, while maintaining data reliability where required.
3Reliability
If multiple error recovery schemes are tried to ensure data recovery, then data recovery capability is improved, but latency increases
Solution Approach 1:
The storage device performs preliminary assessment of data quality metrics to predict which error recovery scheme is most likely to succeed. This allows the system to skip trying multiple schemes and directly apply the most appropriate one, reducing latency while maintaining high recovery capability.
Solution Approach 2:
The system uses data quality metrics as a proxy or copy of the actual data condition, allowing it to determine the appropriate recovery scheme without having to actually attempt multiple recovery operations. This metric-based decision-making avoids the time cost of trial-and-error recovery attempts.
4Reliability
If multiple error recovery schemes are applied to improve data recovery, then data recovery capability is improved, but power consumption increases
Solution Approach 1:
The storage device performs preliminary assessment of data quality metrics to determine the most appropriate error recovery scheme before execution. This prevents wasting power on multiple recovery attempts when the metrics indicate a single scheme is sufficient, while ensuring adequate power is used when complex recovery is needed.
Solution Approach 2:
The system adjusts the intensity and type of error recovery applied based on data quality metrics, using more powerful recovery methods only when metrics indicate poor data quality. This dynamic parameter adjustment optimizes power consumption while maintaining recovery capability.
Data Source
AI summary
A storage device is disclosed. The storage device may include storage for data. A controller may manage writing the data to the storage and reading the data from the reading storage. A data quality metric table may map a first number of errors to a first data quality metric and map a second number of errors to a second data quality metric. A transmitter may return the data quality metric table to a host.