SSD LDPC Decoding Using Syndrome Weight to Cut Read Latency
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Solution Overview
Problem
Conventional Low-Density Parity Check (LDPC) error correction in Solid State Drive (SSD) storage devices is inefficient, leading to extended error correction times and operational interference, necessitating a more effective error correction system.
Innovation Solution
A syndrome-weight-based error correction engine performs hard decoding operations with read voltage thresholds, identifies the lowest syndrome weight among final codeword candidates, and skips or modifies subsequent error correction stages to expedite the error correction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional LDPC error correction performs multiple hard decoding operations followed by soft decoding operations, then error correction reliability is improved, but error correction time is extended
Solution Approach 1:
The patent applies the skipping principle by enabling the error correction system to bypass soft decoding operations when hard decoding operations successfully correct errors. The syndrome weight calculation provides a metric to determine when further soft decoding is unnecessary, allowing the system to rush through the error correction process by skipping redundant operations while maintaining reliability.
Solution Approach 2:
The patent implements partial action by performing only the necessary portion of the error correction process. Instead of always executing the full sequence of hard decoding followed by soft decoding, the system performs partial error correction using only hard decoding when sufficient, and adds soft decoding only when needed based on syndrome weight thresholds, thus optimizing the balance between reliability and time.
2Measurement precision
If error correction operations are performed using multiple read voltage thresholds, then error correction accuracy is improved, but operational interference increases
Solution Approach 1:
The patent applies partial action by using multiple read voltage thresholds only when necessary. The syndrome weight calculation determines whether the full multi-threshold approach is needed or if a single threshold suffices. This partial application of multiple thresholds reduces operational interference while maintaining accuracy when required.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting read voltage thresholds based on syndrome weight calculations. Instead of always using multiple fixed thresholds, the system changes the voltage threshold parameters adaptively - using single or multiple thresholds depending on the error conditions detected, thus reducing operational interference while preserving correction accuracy.
3Reliability
If soft decoding operations are performed after hard decoding operations, then error correction capability is improved, but read latency increases
Solution Approach 1:
The patent applies the skipping principle by allowing the system to bypass soft decoding operations entirely when hard decoding successfully corrects errors, as determined by syndrome weight thresholds. This skipping of unnecessary soft decoding operations significantly reduces read latency while maintaining error correction capability when needed.
Solution Approach 2:
The patent implements partial action by performing soft decoding operations only partially or selectively - specifically, only when the syndrome weight of the hard decoding result exceeds a predetermined threshold. This selective application of soft decoding maintains error correction capability for difficult errors while avoiding the latency penalty for easier cases.
Data Source
AI summary
A storage device syndrome-weight-based error correction system includes a syndrome-weight-based error correction subsystem coupled to a storage subsystem in a chassis. The syndrome-weight-based error correction subsystem performs a plurality of respective first error correction hard decoding operations on the storage subsystem that each utilize respective read voltage thresholds and that each generate a respective final codeword candidate having a respective syndrome weight. The syndrome-weight-based error correction subsystem identifies a first syndrome weight of a first final codeword candidate that was generated via the performance of one of the plurality of respective first error correction hard decoding operations that utilized first read voltage thresholds and that is lower than the syndrome weights of the final codeword candidates generated via the performance of the others of the plurality of respective first error correction hard decoding operations, and performs error correction soft decoding operations using the first read voltage thresholds.