Multi-Level Parity Sectors for Data Recovery
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Solution Overview
Problem
Existing data storage devices face inefficiencies in decoding codewords due to reliance on Viterbi-type detectors, which can increase signal noise, whereas updating reliability metrics using parity sectors is more effective in compensating for signal noise sources.
Innovation Solution
Emphasizing the use of parity sectors over Viterbi-type detectors for updating reliability metrics during decoding, particularly by employing low-density parity check (LDPC) decoders and generating multiple parity sectors to optimize codeword recovery across data segments and interleaves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Viterbi-type detectors are used for decoding codewords, then data can be recovered, but signal noise increases
Solution Approach 1:
The patent segments the error correction process into two distinct components: LDPC decoding for handling signal noise and Viterbi detection for timing and signal recovery. By separating these functions, the system can apply the most appropriate method for each specific problem, using LDPC's superior noise compensation capabilities while reserving Viterbi for its strengths in timing synchronization.
Solution Approach 2:
The patent changes the decoding parameters by implementing multiple decoding passes with different confidence thresholds. The system first attempts LDPC decoding with standard parameters, then for un-converged codewords, applies LDPC again with adjusted parameters, and only as a last resort uses Viterbi detection. This parameter adjustment strategy optimizes noise compensation while minimizing noise introduction.
2Reliability
If multiple parity sectors are generated and processed, then codeword recovery performance improves, but processing time and complexity increase
Solution Approach 1:
The patent performs preliminary LDPC decoding attempts on all codewords using available parity sectors before resorting to more time-consuming methods. By establishing a first-pass recovery using the most efficient algorithm (LDPC), the system minimizes the number of codewords that require additional processing, thereby reducing overall processing time while still achieving high recovery performance.
Solution Approach 2:
The system implements a feedback mechanism where the convergence status of each codeword is evaluated after each decoding pass. Codewords that fail to converge trigger additional LDPC passes with adjusted parameters, and only those remaining un-converged proceed to Viterbi detection. This feedback-driven approach ensures that time-consuming operations are applied only when necessary, optimizing the balance between recovery performance and processing speed.
Data Source
AI summary
A data storage device is disclosed comprising a head actuated over a disk. A first plurality of codewords and corresponding parity sector are generated, and a second plurality of codewords and corresponding parity sector are generated. The first and second plurality of codewords are written to the disk, and during a read of the first and second set of codewords, M codeword locations within the data track that are unrecoverable are saved, and N codeword locations out of the M codeword locations are selected based on a quality metric of the read. The N codewords are reread from the data track at the N codeword locations and reliability metrics associated with the N codewords are saved. The saved reliability metrics are updated using at least one of the first parity sector or the second parity sector.