Non-Volatile Memory Decoding With Adaptive Noise Estimation
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Solution Overview
Problem
Current memory systems face limitations in enhancing error correction decoding capabilities, particularly in accurately estimating noise and decoding data from non-volatile memory systems.
Innovation Solution
A memory system that includes a non-volatile memory and a memory controller, which uses multiple noise estimation models to estimate noise, selects the most accurate noise estimation value, and updates the received word by subtracting this value before decoding using a belief-propagation method to improve decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single noise estimation model is used, then the device complexity is low, but the measurement precision of noise estimation is insufficient
Solution Approach 1:
The decoder is segmented into multiple parallel noise estimation models (first noise estimation model and second noise estimation model), each specialized for different noise conditions. This segmentation allows each model to focus on specific noise characteristics, improving overall estimation accuracy without requiring a single overly complex model
Solution Approach 2:
The system changes parameters by selecting different estimation models based on noise variance thresholds. When noise variance exceeds a threshold, the first model is used; otherwise, the second model is used. This parameter-based selection optimizes measurement precision across different operating conditions while managing complexity through conditional logic
2Reliability
If multiple noise estimation models are used, then the error correction capability is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary noise variance estimation before selecting the appropriate decoding model. This preliminary action allows the system to quickly determine which model is appropriate without trial-and-error, reducing the time penalty of having multiple models by making the selection process efficient and predetermined
Solution Approach 2:
The decoder dynamically switches between different noise estimation models based on real-time noise variance assessment. This dynamic adaptation allows the system to use the simpler second model when conditions permit, reducing processing time, while switching to the more robust first model only when necessary for maintaining error correction capability
3Measurement precision
If noise estimation is performed before decoding, then the decoding accuracy is improved, but the processing complexity increases
Solution Approach 1:
Noise estimation acts as an intermediary step between data reception and decoding. By introducing this intermediate processing stage, the system obtains accurate noise characteristics that inform the subsequent decoding process, improving decoding accuracy while managing complexity through a structured multi-stage approach
Data Source
AI summary
A memory system includes a non-volatile memory and a memory controller. The memory controller is configured to read a received word from the non-volatile memory, estimate noise by using a plurality of different models for estimating the noise included in the received word to obtain a plurality of noise estimation values, select one noise estimation value from the plurality of noise estimation values, update the received word by using a value obtained by subtracting the selected noise estimation value from the read received word, and decode the updated received word by using a belief-propagation method.