Memory Controller Syndrome Weight Reliability
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Solution Overview
Problem
Memory systems face challenges in executing robust error correction using hard-decision read data, as existing methods often rely on soft-decision decoding when hard-decision decoding fails, leading to inefficiencies in error correction processes.
Innovation Solution
A controller and operating method that calculates and utilizes first and second syndrome weights for hard-decision read data from a memory area, determining reliability data based on these weights to perform robust error correction, even when different read biases are applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soft-decision decoding is used when hard-decision decoding fails, then error correction capability is improved, but system complexity and processing time increase
Solution Approach 1:
The patent dynamically adjusts the decoding strategy by first attempting hard-decision decoding, and only transitioning to soft-decision decoding when hard-decision decoding fails. This dynamic approach optimizes the balance between error correction capability and system complexity by using the simpler hard-decision method whenever possible, while resorting to the more complex soft-decision method only when necessary.
Solution Approach 2:
The patent performs hard-decision decoding as a preliminary step before attempting soft-decision decoding. By pre-assessing whether errors can be corrected using the simpler hard-decision method, the system avoids the unnecessary complexity of soft-decision decoding when it is not needed, thus resolving the contradiction between reliability and complexity.
2Reliability
If multiple read operations with different read biases are performed, then error correction robustness is improved, but read time and energy consumption increase
Solution Approach 1:
The patent dynamically determines the number of read operations and selects read bias values based on the actual error characteristics of the stored data. Instead of always performing multiple reads with different biases, the system adapts the read strategy to the specific situation, performing multiple reads only when necessary to achieve robust error correction while minimizing unnecessary read time.
Solution Approach 2:
The patent changes the read bias parameter across multiple read operations to improve error correction robustness. By varying the read bias and recalculating syndrome weights for each read, the system can identify and correct errors more effectively. However, this parameter change is applied selectively based on error detection needs, thus managing the trade-off with read time.
3Measurement precision
If syndrome weight calculation is performed for multiple read data sets, then reliability data accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent calculates syndrome weights for multiple read data sets with different read biases to improve reliability data accuracy. By changing the read bias parameter and recalculating syndrome weights for each read, the system obtains more accurate reliability information. This parameter change approach is applied selectively based on error correction needs, managing the trade-off with processing complexity.
Solution Approach 2:
The patent uses syndrome weight as an intermediary metric to assess reliability without requiring full soft-decision decoding. By calculating syndrome weights for different read data sets and using these as intermediate reliability indicators, the system achieves accurate reliability assessment while avoiding the full complexity of multiple soft-decision decoding operations.
Data Source
AI summary
A controller and an operating method of the controller may calculate a first syndrome weight which is syndrome weight for first read data, calculate a second syndrome weight which is syndrome weight for second read data, and determine first reliability data and second reliability data based on the first syndrome weight and the second syndrome weight. The first read data may be read from a memory area using a first read bias and the second read data may be read from the memory area using a second read bias different from the first read bias.