Adaptive LLR Table Updating for Memory Decoding Failures
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Solution Overview
Problem
In memory systems, decoding failures often occur due to mismatches between assumed and actual channel conditions, leading to increased decoding errors and reduced decoding capabilities.
Innovation Solution
The implementation of a memory system that generates an estimated LLR table based on failed decoding results, using the estimated channel transition matrix to approximate the correct channel, thereby recovering from decoding failures and improving decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed LLR table is used for decoding, then the decoding process is simple and fast, but decoding failures occur due to mismatches between assumed and actual channel conditions
Solution Approach 1:
The patent applies dynamics by making the LLR table adaptive rather than fixed. The system dynamically generates and updates the LLR table based on actual channel conditions observed during decoding operations. When decoding failures are detected, the system adjusts the LLR table parameters to better match the actual channel, thereby improving reliability while maintaining manageable complexity through controlled adaptation.
Solution Approach 2:
The patent implements feedback mechanisms where decoding results are monitored and used to update the LLR table. When decoding failures occur, the system uses the failure information to adjust the LLR table parameters for subsequent decoding attempts. This closed-loop feedback approach ensures that the LLR table continuously adapts to actual channel conditions, resolving the contradiction between reliability and complexity.
2Reliability
If the LLR table is updated frequently to match actual channel conditions, then decoding accuracy improves, but processing time increases
Solution Approach 1:
The patent applies periodic action by updating the LLR table at specific intervals or under specific conditions rather than continuously. The system monitors decoding performance and triggers LLR table updates only when necessary (e.g., when decoding failures are detected or after a certain number of operations). This periodic update strategy maintains high decoding accuracy while minimizing the time overhead associated with frequent updates.
Solution Approach 2:
The patent uses parameter changes by adjusting specific parameters of the LLR table based on channel conditions rather than completely regenerating the table. The system modifies key parameters such as the mean and variance of the Gaussian distribution used in LLR calculation, allowing for efficient updates that improve accuracy without requiring full recalculation, thus reducing processing time overhead.
Data Source
AI summary
According to one embodiment, a memory system includes a non-volatile memory, a memory interface that reads data recorded in the non-volatile memory as a received value, a converting unit that converts the received value to first likelihood information by using a first conversion table, a decoder that decodes the first likelihood information, a control unit that outputs an estimated value with respect to the received value, which is a decoding result obtained by the decoding, when decoding by the decoder has succeeded, and a generating unit that generates a second conversion table based on a decoding result obtained by the decoding, when decoding of the first likelihood information by the decoder has failed. When the generating unit generates the second conversion table, the converting unit converts the received value to the second likelihood information by using the second conversion table, and the decoder decodes the second likelihood information.