ECC Memory Error Pattern Analysis for Uncorrectable Fault Prediction
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Solution Overview
Problem
Existing methods for identifying non-correctable errors in memory devices rely on error count totals or rates, leading to inaccurate identification of vulnerable memory regions, resulting in false positives and negatives.
Innovation Solution
A pattern-based approach that identifies groups of memory cells with errors and compares them to known correctable and non-correctable error patterns using error correction codes (ECC) to determine vulnerability to future non-correctable errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If error count totals or rates are used to identify non-correctable errors, then the identification process is simple, but the accuracy is poor leading to false positives and negatives
Solution Approach 1:
The patent segments the error identification process into multiple stages: collecting error data from memory cells, analyzing error patterns using ECC, comparing against known correctable/non-correctable patterns, and making vulnerability determinations. This segmentation transforms a simple but inaccurate count-based approach into a complex but accurate pattern-based analysis system.
2Reliability
If pattern-based analysis is used to improve accuracy of non-correctable error identification, then false positives and negatives are reduced, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and storing error data from memory cells, pre-analyzing error patterns using ECC, and pre-comparing against databases of known correctable and non-correctable error patterns. This preliminary analysis is done before final vulnerability determination, reducing the computational burden during critical decision-making and improving overall reliability.
3Productivity
If traditional error counting methods are used, then resource usage is low, but unnecessary remedial actions are triggered due to false positives
Solution Approach 1:
The patent implements feedback mechanisms where error data is continuously collected from memory cells, analyzed using ECC algorithms, compared against known patterns, and used to update vulnerability determinations. This feedback loop ensures that remedial actions are triggered only when actual vulnerabilities are detected, not due to false positives from simple error counting, thereby improving both reliability and productive efficiency.
Data Source
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AI summary
Techniques are described for identifying patterns of memory cells in a memory array that are predictive of non-correctable errors ("corruption patterns"). The techniques described herein identify patterns of cell errors that are likely to generate errors that cannot be corrected by an error correction code (ECC). The identification of non-correctable cells is accomplished by identifying a pattern of cell errors storing bit values that deviate from corresponding expected values. The pattern of these memory cells and various combinations of the cells in the pattern are compared to patterns of cells that are known to be correctable using ECC. If the error pattern or one or more of the combinations of erroneous cells in the pattern are not associated with patterns that are correctable via ECC, the error pattern is identified as predictive of a likely uncorrectable error.