Error Correction Decoder With UCN Threshold Iteration Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage devices face challenges in efficiently performing error correction decoding, particularly in determining when to terminate the decoding process to ensure data reliability and minimize unnecessary iterations.
Innovation Solution
A storage device with a memory controller that iterates error correction decoding on read data, using threshold comparisons with Unsatisfied Check Nodes (UCNs) in the syndrome to determine whether to continue or terminate the decoding process, with multiple sets of iterations and threshold values used for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error correction decoding is iterated multiple times to ensure data reliability, then data reliability is improved, but decoding time and processing overhead increase
Solution Approach 1:
The patent implements a feedback mechanism where the syndrome result from each decoding iteration is evaluated against a threshold. The number of Unsatisfied Check Nodes (UCNs) in the syndrome serves as feedback to determine whether to continue iterating or terminate decoding. This feedback-based early termination resolves the contradiction by stopping iterations when the syndrome indicates sufficient reliability, avoiding unnecessary additional decoding time while maintaining data reliability standards.
2Reliability
If error correction decoding is iterated more times to correct difficult errors, then error correction capability is improved, but processing overhead and complexity increase
Solution Approach 1:
The patent dynamically changes the decoding parameters based on syndrome evaluation. Specifically, it monitors the number of Unsatisfied Check Nodes (UCNs) in the syndrome and compares against a threshold to adaptively determine iteration continuation. This parameter-based control resolves the contradiction by adjusting processing intensity according to actual error conditions, maintaining high error correction capability when needed while reducing processing overhead when errors are few or easily correctable.
Solution Approach 2:
The patent applies partial action by performing only the necessary number of decoding iterations required to achieve reliable error correction. Instead of always executing a fixed maximum number of iterations, it performs partial iterations based on syndrome evaluation, stopping early when the threshold is met. This resolves the contradiction by avoiding excessive processing overhead while maintaining sufficient error correction capability through targeted iterations.
3Reliability
If threshold values are set low to ensure high reliability, then data reliability is improved, but more iterations are required increasing decoding time
Solution Approach 1:
The patent uses feedback from syndrome evaluation to dynamically adjust the effective threshold requirement. By monitoring the number of Unsatisfied Check Nodes (UCNs) and comparing against the threshold, the system receives feedback that guides iteration continuation decisions. This feedback mechanism resolves the contradiction by allowing the system to meet reliability requirements through intelligent threshold evaluation rather than simply increasing iteration count, thereby maintaining decoding throughput.
4Measurement precision
If multiple sets of iterations with different threshold values are used for verification, then decoding accuracy is improved, but processing complexity and time increase
Solution Approach 1:
The patent applies partial action by performing verification iterations only when necessary. Instead of always executing multiple verification passes with different thresholds, it performs partial verification based on the initial decoding result and syndrome evaluation. This selective approach resolves the contradiction by achieving sufficient decoding accuracy through targeted verification rather than exhaustive multi-threshold checking, thereby reducing verification time while maintaining accuracy.
Data Source
AI summary
A storage device may include a memory device and a memory controller. The memory device may store data. The memory controller may iterate error correction decoding on data read from the memory device, determine whether to continue iterating based on a result obtained by comparing a first threshold number with a number of Unsatisfied Check Nodes (UCNs) included in a syndrome of first decoded data, which is a result of a first set number of iterations of the error correction decoding on the read data, and determine whether to continue iterating based on a result obtained by comparing a second threshold number with a number of UCNs included in a syndrome of second decoded data, which is a result of a sum of the first set number and a second set number of iterations of the error correction decoding on the read data.