RAM Fail Data Augmentation for ECC-Limited Failure Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The decreasing frequency of failures in random access memories due to Error Correction Code (ECC) and product advancements makes it difficult to secure sufficient fail data for analyzing memory failures using artificial intelligence.
Innovation Solution
A fail data augmentation device and method that includes a memory to store fail data units and a data augmentation model to generate augmented fail data units, processed by a processor to filter and delete unnecessary data, ensuring accurate fail data generation for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Error Correction Code (ECC) and product advancements are implemented, then reliability of random access memory is improved, but frequency of failure decreases making it difficult to secure sufficient fail data
Solution Approach 1:
The patent uses a data augmentation model to generate synthetic fail data units that copy and transform existing fail data units. The model creates augmented fail data units by applying transformation parameters to original fail data, effectively replicating failure patterns without needing actual physical failures. This resolves the contradiction by producing sufficient fail data quantity through virtual copying rather than relying on rare actual failures.
Solution Approach 2:
The patent transforms fail data units by changing parameters such as failure modes, error patterns, and data characteristics. The data augmentation model applies various parameter transformations to generate diverse fail data units from a limited set of original failures. This allows the system to maintain high reliability through ECC while generating sufficient training data by exploring different parameter spaces of potential failures.
2Quantity of substance
If data augmentation model generates augmented fail data units, then quantity of fail data is increased, but accuracy of fail data may be reduced due to generation errors
Solution Approach 1:
The patent implements a filtering mechanism that provides feedback on the quality of generated augmented fail data units. The processor evaluates each augmented fail data unit against predefined criteria and filters out units that do not meet accuracy thresholds. This feedback loop ensures that only high-quality augmented data is retained, maintaining measurement precision while still increasing the overall quantity of useful fail data for analysis.
Solution Approach 2:
The patent applies different quality standards and filtering criteria to different portions or types of fail data units. Rather than uniformly treating all augmented data the same, the system applies local quality control tailored to specific failure modes and data characteristics. This allows the system to maintain high accuracy for critical failure patterns while being more lenient with less critical variations, thereby preserving overall data accuracy while expanding data quantity.
3Adaptability or versatility
If fail data units are processed through data augmentation model, then diversity of fail data is improved, but complexity of data processing increases
Solution Approach 1:
The patent segments the data processing workflow into distinct modular components: the data augmentation model for generating augmented data, the filtering mechanism for quality control, and the processor for executing operations. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high data diversity. The modular architecture makes the complex processing pipeline more manageable and easier to implement.
Data Source
AI summary
A fail data augmentation device may input a plurality of fail data units to a data augmentation model, obtain a plurality of augmented fail data units outputted from the data augmentation model, and delete one or more of the augmented fail data units. The plurality of fail data units and the plurality of augmented fail data units includes a first parameter indicating one of a plurality of banks included in a random access memory, a second parameter indicating one of a plurality of matrices included in the bank corresponding to the first parameter, and a third parameter indicating one of a plurality of hex units included in the matrix corresponding to the second parameter respectively.