Memory Controller Feature Boosting for NAND Error Correction
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Solution Overview
Problem
Current semiconductor memory systems face challenges in accurately correcting errors in NAND memory due to internal data corruption caused by electrical or magnetic interference and degradation, which existing Error Correction Code (ECC) controllers struggle to address effectively, especially with increasing demands for performance and security.
Innovation Solution
A semiconductor memory system and operating method that utilize a memory controller with a feature booster and linear predictor to model NAND data using a mixture model with latent variables, allowing for accurate prediction of failed bits and feature information, employing Gaussian mixture modeling and Student-t distributions to improve error correction capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECC controllers are used for error correction, then the system structure remains simple, but the error correction accuracy deteriorates due to inability to handle internal data corruption from NAND degradation
Solution Approach 1:
The controller is segmented into multiple functional modules: a mixture model construction unit that creates probabilistic models of NAND data patterns, a feature booster that extracts relevant features from raw data, and a linear predictor that uses these features to predict error locations. This segmentation allows each module to specialize in a specific aspect of error correction, improving overall accuracy while keeping individual module complexity manageable.
Solution Approach 2:
The patent introduces an intermediary layer between the NAND memory and the traditional ECC controller. This intermediary consists of the mixture model and feature extraction mechanisms that preprocess the data and provide enhanced error patterns to the ECC controller. This intermediary layer bridges the gap between simple ECC structures and the need for high accuracy by transforming raw data into more informative representations.
2Measurement precision
If mixture model with latent variables is used to model NAND data, then the error prediction accuracy is improved, but the computational complexity increases
Solution Approach 1:
The mixture model with latent variables is constructed and trained in advance during a preliminary phase, capturing the statistical characteristics of NAND data degradation patterns. Once trained, the model can quickly predict error locations during normal operation without requiring complex real-time computations. This preliminary action separates the heavy computational work from the operational phase, reducing power consumption during actual error correction.
Solution Approach 2:
The patent employs parameter changes by using different distribution models (Gaussian, Student-t, Gamma distributions) for different latent variables in the mixture model. By selecting appropriate distribution types and adjusting their parameters based on the specific degradation patterns observed, the system optimizes prediction accuracy while managing computational complexity through parameter optimization rather than structural complexity.
3Reliability
If feature booster and linear predictor are employed, then the system reliability is improved through better error correction, but the device complexity increases
Solution Approach 1:
The feature booster and linear predictor are designed as universal modules that can handle multiple types of NAND degradation patterns and error conditions through a unified framework. The mixture model can accommodate different distribution types for different latent variables, allowing the same architectural structure to adapt to various degradation scenarios. This universality reduces the need for multiple specialized components, thereby managing complexity while maintaining high reliability across different operating conditions.
Data Source
AI summary
A semiconductor memory system and an operating method thereof include a plurality of memory devices; and a memory controller including a feature booster and a linear predictor and coupled with the plurality of memory devices, wherein the controller is configured to collect NAND data from at least 1 data point, and model the collected NAND data with a mixture model, wherein the mixture model includes parameters and at least two latent variables modeled with different distribution modeling, the feature booster is configured to predict the parameters, and the linear predictor is configured to predict feature information.