Storage Defect Scanning With Error Ratio and Moving Windows
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Solution Overview
Problem
Low-density parity-check (LDPC) codes pose challenges in designing manufacturing tests for storage devices due to their unpredictable defect behavior and lack of bounded distance decoding, making it difficult to determine if a sector is close to failure and whether reserved margins are sufficient for handling growing defects.
Innovation Solution
Implementing a defect test using an error ratio (ER) calculation and a moving window approach to detect defects by comparing the number of error bits and symbols with thresholds, ensuring that sectors with high error ratios or excessive error bits are identified as defective, thereby maintaining quality over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional defect testing methods are used for LDPC codes, then the testing process is simple, but the measurement precision is insufficient due to unpredictable defect behavior and lack of bounded distance decoding
Solution Approach 1:
The patent changes the testing parameters by introducing error ratio (ER) calculation and moving window analysis instead of traditional fixed-threshold methods. The error ratio is calculated as ER = (number of error bits) / (number of decoded symbols), and the moving window approach slides through the data to detect localized defect patterns. These parameter changes enable precise detection of unpredictable defect behaviors in LDPC codes while maintaining a systematic testing framework.
Solution Approach 2:
The patent segments the storage medium into multiple sectors and applies moving window analysis to examine error patterns within specific segments. By dividing the testing into sector-level and window-level analyses, the method can identify localized defects without requiring complex global analysis, thus improving detection precision while keeping the testing approach manageable.
2Reliability
If conservative defect masking is applied to ensure quality, then reliability is improved, but productivity decreases due to excessive sectors being masked out
Solution Approach 1:
The patent replaces the mechanical threshold-based masking approach with a statistical analysis system. Instead of masking sectors based on fixed error counts, the system calculates error ratios and analyzes error distributions using moving window techniques. This substitution allows for more nuanced decision-making, masking only sectors with genuine defect patterns rather than conservatively masking all sectors with any errors, thus maintaining reliability while preserving more usable storage capacity.
Solution Approach 2:
The patent implements feedback through iterative error ratio calculation and moving window analysis. The testing process continuously refines its understanding of defect patterns by analyzing error distributions across multiple windows and sectors. This feedback mechanism allows the system to distinguish between random errors and systematic defects, enabling selective masking that maintains quality standards while minimizing capacity loss.
3Difficulty of detecting and measuring
If traditional error counting methods are used, then the ease of operation is maintained, but the difficulty of detecting and measuring defects increases due to unpredictable defect behavior
Solution Approach 1:
The patent introduces error ratio as an intermediary metric between raw error counts and defect detection decisions. Instead of directly counting errors and making masking decisions, the system first calculates ER = (number of error bits) / (number of decoded symbols), then uses this ratio within moving window analysis. This intermediary approach transforms unpredictable error patterns into a standardized metric that reveals defect patterns more clearly, making detection easier without significantly complicating the overall process.
Data Source
AI summary
A method for detecting a defect in a portion of a storage device is disclosed. Reference data and data read from the portion are compared to determine a number of error bits and a number of error symbols. An error ratio is computed, wherein the error ratio comprises a ratio of the number of error bits to the number of error symbols. A defect is detected based on whether the error ratio exceeds a threshold. In some embodiments, the reference data and the read data are compared to determine an error vector, wherein a bit in the error vector with a value one indicates a bit error in the read data. For each of a plurality of windows of the error vector, a corresponding number of error bits is determined. A defect is detected based on whether any of the numbers of error bits exceeds a threshold.