Soft-Decision Decoding with Adaptive LLR Table Updates
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Solution Overview
Problem
Soft-decision decoding in nonvolatile memory systems faces challenges due to fluctuations in threshold voltage distribution, leading to decreased error correction performance and decoding failures, as existing methods like dynamic LLR estimation are limited in handling all phenomena causing decoding failure.
Innovation Solution
A decoding device and method that includes a converter for converting data using a first conversion table, a decoder for decoding likelihood information, and a creator for generating a second conversion table when decoding fails, allowing for repeated soft-decision decoding with updated LLR tables to improve error correction and reduce decoding failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed LLR table is used for soft-decision decoding, then the decoding process is simple and fast, but the error correction performance decreases when threshold voltage distribution changes
Solution Approach 1:
The patent implements dynamic LLR table generation that adapts to changing threshold voltage distributions. Instead of using a fixed LLR table, the system dynamically creates updated LLR tables based on actual memory cell characteristics, thereby maintaining high error correction performance while managing complexity through targeted updates rather than continuous regeneration.
Solution Approach 2:
The patent changes the parameters of the LLR table based on observed threshold voltage distributions. By monitoring actual memory cell behavior and adjusting the LLR table parameters accordingly, the system maintains optimal error correction performance without requiring complete redesign of the decoding process.
2Reliability
If dynamic LLR estimation is used to adapt to threshold voltage changes, then error correction performance is maintained, but the decoding process becomes more complex and time-consuming
Solution Approach 1:
The patent performs preliminary LLR table generation and stores updated tables for future use. By pre-computing LLR tables based on threshold voltage distribution characteristics before actual decoding operations, the system reduces the time required during actual decoding while maintaining adaptive error correction performance.
Solution Approach 2:
The patent applies dynamic LLR estimation selectively rather than universally. By identifying specific regions or conditions where threshold voltage changes occur and applying adaptive LLR table generation only in those cases, the system maintains error correction performance where needed while minimizing the time overhead across the entire decoding process.
3Measurement precision
If the LLR table is updated frequently to track threshold voltage distribution changes, then decoding accuracy is maintained, but the processing overhead increases
Solution Approach 1:
The patent implements periodic LLR table updates rather than continuous updates. By updating the LLR table at predetermined intervals or based on specific triggering conditions (such as detected threshold voltage shifts), the system maintains sufficient LLR value accuracy while minimizing the processing overhead associated with frequent table regeneration.
Data Source
AI summary
According to one embodiment, a decoding device comprises a converter configured to convert read data to first likelihood information by using a first conversion table, a decoder which decodes the first likelihood information, a controller which outputs a decoding result of the decoder when the decoder succeeds decoding, and a creator module which creates a second conversion table based on the decoding result when the decoder fails decoding. When the second conversion table is created, at least a part of the decoding result is converted to second likelihood information by using the second conversion table the second likelihood information is decoded.