Flash Memory Data Retention Detection and Block Isolation
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Solution Overview
Problem
Data stored in flash memory devices becomes less reliable over time due to charge leakage and shifting threshold voltages, leading to errors and potential data corruption, especially in multi-level cell (MLC) blocks, which can result in data loss if not managed properly.
Innovation Solution
A data retention detection technique that monitors error rates in blocks of a data storage device, setting a flag when a threshold is exceeded, allowing the controller to close the block to further write operations, thereby isolating stressed data and using a common set of parameters for reading, reducing latency and preventing data corruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in flash memory blocks over time, then storage capacity is utilized, but data reliability deteriorates due to charge leakage and threshold voltage shifting
Solution Approach 1:
The system performs preliminary detection of data retention status by monitoring error rates in reference pages before data corruption becomes critical. The controller periodically senses reference pages and compares error rates to detect degradation trends, allowing preventive action to be taken before data becomes unrecoverable.
Solution Approach 2:
The system extracts and isolates stressed blocks from the active storage pool by closing blocks to write operations when retention degradation is detected. This separates degraded data from healthy data, preventing further writes to stressed blocks while preserving access to reliable blocks.
2Reliability
If blocks are closed to write operations to prevent data corruption, then data integrity is improved, but write productivity is reduced
Solution Approach 1:
The storage device is segmented into multiple blocks, and the system applies selective closure only to specific blocks that show retention degradation. Healthy blocks remain open for normal write operations, while only stressed blocks are closed. This segmented approach minimizes the impact on overall write productivity while protecting data integrity in affected areas.
3Measurement precision
If separate parameter sets are used for stressed and new data, then data accuracy is improved, but access latency increases
Solution Approach 1:
The system extracts and isolates stressed data from the block, separating it from new data that can be written. By closing blocks to write operations, the system ensures that only one set of read parameters is needed for each block, eliminating the complexity of managing multiple parameter sets and reducing access latency.
4Reliability
If error rates are monitored continuously to detect retention degradation, then data reliability is improved, but device complexity increases
Solution Approach 1:
Instead of monitoring all data blocks continuously, the system applies partial monitoring by selecting representative reference pages from each block and monitoring error rates only in those reference pages. This partial action provides sufficient insight into block retention status without the complexity of comprehensive continuous monitoring of all data.
Data Source
AI summary
A data storage device includes a non-volatile memory and a controller. A method includes writing an indication of a first error rate of a first set of bits to the non-volatile memory. The first set of bits is sensed from a word line of the non-volatile memory. The word line is sensed to generate a second set of bits in response to a first power-on event being initiated at the data storage device after writing the indication of the first error rate to the non-volatile memory. The method further includes setting a data retention flag in response to a difference between the first error rate and a second error rate associated with the second set of bits satisfying a threshold.