Dynamic Failure Prediction for Non-Volatile Memory Using Field Tests
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Solution Overview
Problem
Current non-volatile memory (NVM) health diagnostics are inadequate for predicting failures, as they rely on static wear-out models and do not account for actual media state or workload, leading to insufficient quality of service and inability to correlate media cycles with failure risk, resulting in unexpected system degradation or disruption.
Innovation Solution
A memory controller with an analyzer that performs reference and field tests on NVM dies, storing results to predict likelihood and time of failure, allowing for dynamic adjustment and proactive maintenance, and providing risk projections based on cumulative memory cycles and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static wear-out models are used for NVM health diagnostics, then device complexity is reduced, but measurement precision and reliability of failure prediction deteriorate
Solution Approach 1:
The patent transitions from static wear-out models to dynamic failure prediction by continuously monitoring NVM die health metrics during operation and adjusting predictions based on actual workload patterns and media state changes, making the diagnostic system adaptive rather than fixed
Solution Approach 2:
The system implements feedback loops where field test results and actual failure data are fed back into the prediction model, allowing the system to learn from real-world performance and continuously improve prediction accuracy based on correlated media cycles and workload patterns
2Measurement precision
If comprehensive field tests are performed to improve failure prediction accuracy, then measurement precision improves, but productivity and time consumption increase
Solution Approach 1:
The patent performs selective field tests focusing on critical failure modes and high-risk operational conditions rather than exhaustive testing, applying partial action where full comprehensive tests would be overly time-consuming but targeted tests provide sufficient prediction accuracy
Solution Approach 2:
The system implements periodic field tests at strategically determined intervals based on accumulated media cycles and workload intensity, rather than continuous monitoring, allowing the system to balance prediction accuracy with operational productivity by testing when most informative
3Ease of operation
If static wear-out models are used, then ease of operation is maintained, but reliability and quality of service deteriorate due to unexpected failures
Solution Approach 1:
The NVM die performs self-diagnostics through integrated field testing capabilities, automatically monitoring its own health metrics and reporting status without requiring external intervention, maintaining ease of operation while improving reliability through continuous self-assessment
Solution Approach 2:
The system performs preliminary health assessments and failure predictions before actual failures occur, enabling proactive maintenance and data migration initiatives that prevent service disruption, thereby improving reliability while maintaining operational simplicity
Data Source
AI summary
A memory controller includes a memory to store results of a reference performance test (RT) performed on a non-volatile memory (NVM) die, where the results of the RT include one or more first indicators of failure associated with one or more first read/write cycles of the NVM die before the NVM die is placed in use. The memory controller further includes an analyzer coupled with the memory to perform, in one or more second read/write cycles, one or more field tests that provide second indicators of failure associated with one or more second read/write cycles of the NVM die during the use of the NVM die, and further to predict and dynamically adjust, over one or more second read/write cycles, at least one of likelihood or expected time of failure of the NVM, based at least in part on the first and second indicators of failure.