Memory Sub-System Retirement via Fail Bit Count Monitoring
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Solution Overview
Problem
Existing memory sub-system retirement determination methods are oversimplified and do not effectively monitor the performance of a large number of memory cells, leading to premature retirement or continued operation beyond recommended lifespan, lacking real-time data integrity monitoring.
Innovation Solution
Implementing a memory sub-system fail bit count component that monitors error parameter values such as fail bit count and codeword error rate, allowing for real-time retirement determination based on statistical boundaries and write cycles, enabling component-level retirement decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplified retirement determination methods are used, then device complexity is reduced, but measurement precision and reliability of retirement determination deteriorate
Solution Approach 1:
The patent segments the retirement determination process into distinct components: monitoring error parameter values (fail bit count, codeword error rate), comparing against statistical boundaries, and making retirement decisions at component level. This segmentation allows for precise monitoring without requiring complex overall system redesign.
Solution Approach 2:
The patent implements continuous feedback by monitoring error parameter values during write cycles and comparing them against statistical boundaries. This feedback mechanism enables real-time assessment of memory component health and accurate determination of retirement timing based on actual performance data.
2Reliability
If real-time error parameter monitoring is implemented, then reliability of retirement determination is improved, but device complexity increases
Solution Approach 1:
The memory sub-system performs self-monitoring by tracking its own error parameter values (fail bit count, codeword error rate) during normal write cycles. This self-service approach enables reliable retirement determination without requiring external monitoring systems, thus improving reliability while minimizing added complexity.
Solution Approach 2:
The monitoring mechanism serves multiple functions: it tracks error rates, compares against statistical boundaries, determines component health status, and guides retirement decisions. This multi-functionality reduces the need for separate specialized systems, thereby improving reliability without proportionally increasing complexity.
3Reliability
If statistical boundaries and error parameter monitoring are used, then data integrity is improved, but loss of time in retirement determination increases
Solution Approach 1:
The error parameter monitoring operates continuously during normal write cycles without interrupting memory operations. This continuous monitoring ensures data integrity is maintained while avoiding time loss, as the retirement determination is based on accumulated real-time data rather than periodic assessments.
Solution Approach 2:
The system establishes statistical boundaries and monitoring thresholds in advance before actual retirement determination is needed. This preliminary action prepares the framework for quick real-time assessment, ensuring data integrity through pre-defined criteria while minimizing determination time when retirement decisions are actually required.
Data Source
AI summary
A method includes performing a quantity of write cycles on memory components. The method can further include monitoring codewords, and, for each of the codewords including a first error parameter value, determining a second error parameter value. The method can further include determining a probability that each of the codewords is associated with a particular one of the second error parameter values at the first error parameter value and determining a quantity of each of the codewords that are associated with each of the determined probabilities. The method can further include determining a statistical boundary of the quantity of each of the codewords and determining a correlation between the quantity of write cycles performed and the corresponding determined statistical boundary of the quantity of each of the codewords.