LLR Conversion Table Adaptation for Channel-Mismatched Memory Decoding
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Solution Overview
Problem
In memory systems with non-volatile memory, decoding failures due to channel mismatches between assumed and actual channels lead to increased decoding errors, especially when pre-prepared LLR tables are not available for all channels, resulting in deteriorated decoding capabilities.
Innovation Solution
The implementation of a memory system that generates an estimated LLR table based on failed decoding results, using the estimated write value to approximate the correct channel transition matrix, allowing for continued decoding even when initial decoding fails, and potentially modifying the estimated LLR table to improve decoding success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-prepared LLR tables are used for decoding, then decoding speed is improved, but decoding reliability deteriorates when channel mismatches occur
Solution Approach 1:
The patent pre-calculates and stores multiple LLR tables corresponding to different channel conditions before actual decoding operations. When a channel mismatch is detected or anticipated, the system can quickly switch to the appropriate pre-prepared LLR table without performing real-time calculations, thus maintaining both fast decoding speed and high reliability across varying channel conditions.
Solution Approach 2:
The patent changes the parameter of LLR table selection based on detected channel conditions. By monitoring channel characteristics and dynamically selecting or generating the most appropriate LLR table for current conditions, the system adapts to channel variations while maintaining decoding reliability, rather than relying on a single fixed LLR table.
2Reliability
If multiple LLR tables are prepared for different channels, then decoding reliability is improved, but device complexity increases
Solution Approach 1:
The patent designs a universal LLR table generation mechanism that can serve multiple channel types. Instead of maintaining completely separate LLR tables for each channel, the system uses a unified approach where LLR tables can be generated or selected based on channel characteristics, reducing the overall complexity while maintaining reliability across different channel conditions.
Solution Approach 2:
The system incorporates automatic LLR table selection and generation capabilities that operate autonomously based on detected channel conditions. The decoding apparatus itself performs the task of selecting or generating appropriate LLR tables without requiring complex external management, thereby improving reliability while keeping the system self-managing and relatively simple.
3Reliability
If LLR table modifications are performed to improve decoding, then decoding reliability is improved, but processing time increases
Solution Approach 1:
The patent performs LLR table modifications and optimizations in advance, before actual decoding operations begin. By pre-adjusting LLR tables based on historical channel data or initial channel assessments, the system eliminates the need for time-consuming real-time modifications during active decoding, thus maintaining both high reliability and efficient processing speed.
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.