Memory Quality Engine CDF Analysis for Reliability Optimization
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Solution Overview
Problem
Existing memory analysis systems fail to provide adequate metrics for optimizing memory system performance, diagnosing memory quality, and predicting remaining life, and they do not facilitate analytical connections between data sets effectively.
Innovation Solution
A memory quality engine that performs analyses using cumulative distribution function (CDF) based data, allowing for quality measurements at various granularities, comparing CDF-based data to thresholds, and making improvements by classifying memory populations based on health measures and adjusting operating parameters to enhance performance and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing memory analysis systems are used, then basic memory operations can be performed, but adequate metrics for optimizing performance, diagnosing quality, and predicting remaining life are not provided
Solution Approach 1:
The patent segments memory analysis into multiple granularity levels (device level, die level, block level, page level) and applies CDF-based analysis at each level. This segmentation enables comprehensive quality measurement across different hierarchical levels, providing detailed metrics for reliability assessment without information loss.
Solution Approach 2:
The patent replaces traditional histogram-based analysis with CDF-based analysis. This substitution transforms the measurement approach from discrete binning to continuous distribution function analysis, enabling more precise quality metrics and better analytical connections between different memory populations.
2Productivity
If traditional analysis methods are used, then simple memory monitoring is possible, but analytical connections between data sets are not facilitated
Solution Approach 1:
The CDF-based analysis framework provides a universal methodology that can be applied across multiple memory technologies (NAND, NOR, 3D XPOINT), different granularities (device, die, block, page), and various quality metrics. This universality facilitates analytical connections between diverse data sets while maintaining analysis efficiency.
Solution Approach 2:
The patent changes the fundamental parameter representation from histogram bins to CDF values. This parameter transformation enables direct mathematical operations and comparisons between different memory populations, facilitating analytical connections while improving analysis efficiency through standardized metric computation.
3Reliability
If memory populations are not classified, then all memory can be used uniformly, but defective populations cannot be identified
Solution Approach 1:
The patent implements feedback through threshold-based classification of memory populations based on CDF analysis results. Quality metrics are compared against thresholds to classify populations as good, marginal, or bad, enabling identification of defective populations while providing feedback for dynamic remapping and replacement decisions.
Solution Approach 2:
The patent performs preliminary CDF-based quality analysis during memory initialization and wear leveling operations. This preliminary classification identifies defective populations before they cause failures, enabling proactive remapping and replacement strategies that maintain reliability without complex real-time monitoring.
Data Source
AI summary
A memory quality engine can improve the operation of a memory system by setting more effective operating parameters, disabling or removing memory devices unable to meet performance requirements, and providing evaluations between memory populations. These improvements can be accomplished by converting quality measurements of a memory population into CDF-based data, formulating comparisons of the CDF-based data to metrics for quality analysis, and applying the quality analysis. In some implementations, the metrics for quality analysis can use one or more thresholds, such as a system trigger threshold or an uncorrectable error correction condition threshold, which are set based on the error correction capabilities of a memory system. Formulating the comparison to these metrics can include determining an intersection between the CDF-based data and one of the thresholds.