Targeted Auto Read Calibrate for Memory Subsystems
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Solution Overview
Problem
Memory sub-systems, such as solid-state drives, often fail to correctly read data due to changes in temperature or degradation of charge levels in memory cells, leading to longer read retry processes with conventional auto read calibrate operations.
Innovation Solution
Implementing a targeted auto read calibrate operation that selects a baseline threshold voltage set based on syndrome weights generated by an error correction component, reducing the execution of unnecessary auto read calibration operations and shortening the read retry process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional auto read calibrate operations are executed, then data recovery is attempted, but the read retry process becomes longer and less efficient
Solution Approach 1:
The system performs preliminary actions by executing read retry operations with multiple threshold voltage sets before initiating auto read calibrate operations. Syndrome weights are generated and stored in advance to guide subsequent calibration, ensuring that the calibration process starts with optimized parameters rather than brute-force searching, thereby reducing overall read retry time while maintaining data recovery reliability
Solution Approach 2:
The system implements feedback mechanisms by using syndrome weights from read retry operations to dynamically adjust and select baseline threshold voltage sets for auto read calibrate operations. This feedback loop allows the system to learn from previous read attempts and optimize future calibration processes, reducing unnecessary calibration iterations and shortening the read retry process time
2Reliability
If multiple auto read calibrate operations are executed, then read retry success rate improves, but unnecessary operations increase device complexity and time consumption
Solution Approach 1:
The system applies local quality by selecting specific baseline threshold voltage sets for auto read calibrate operations based on syndrome weight analysis rather than uniformly applying all possible calibration operations. This targeted approach ensures that calibration operations are localized to the most promising voltage ranges, reducing unnecessary operations and simplifying the overall process while maintaining high read retry success rates
Solution Approach 2:
The system utilizes parameter changes by dynamically selecting and adjusting threshold voltage sets based on syndrome weights from read retry operations. This parameter optimization allows the system to focus calibration efforts on the most relevant voltage ranges, reducing the number of unnecessary calibration operations and simplifying the device complexity while preserving read retry effectiveness
Data Source
AI summary
Various examples are directed to systems and methods for reading a memory component. A processing device may receive an indication that a read operation at a physical address of the memory component failed. The processing device may execute a plurality of read retry operations at the physical address. The processing device may access a first syndrome weight describing a first error correction operation performed on a result of a first read retry operation of the plurality of read retry operations and a second syndrome weight describing a second error correction operation performed on a result of a second read retry operation of the plurality of read retry operations. The processing device may select a first threshold voltage associated with the first read retry operation based at least in part on the first syndrome weight and the second syndrome weight. The processing device may also execute a first auto read calibrate operation at the physical address, the first auto read calibrate operation having a baseline at the first threshold voltage.