Bad Data Block Selection in Flash Storage
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Solution Overview
Problem
The quality of sample data blocks in data storage devices significantly impacts statistical feature parameters, particularly in Downgrade Flash, necessitating a method to identify and remove bad data blocks that induce deviation.
Innovation Solution
A method for selecting bad data blocks in a data storage device involves writing data to a sample block, comparing written and read data to calculate error bits, identifying chunks with the largest error bits, and recording blocks exceeding threshold values for error correction and recordable bad data columns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sample data blocks are used to evaluate data storage device quality, then statistical feature parameters can be obtained, but bad data blocks may induce deviation in these parameters
Solution Approach 1:
The patent applies preliminary action by performing a write-read-verify operation on sample data blocks before using them for evaluation. The control unit writes test data to each data block, reads it back, and verifies correctness by comparing written and read data. This preliminary verification identifies and excludes bad data blocks before they can contaminate the statistical feature parameter calculations, ensuring both measurement precision and reliability.
2Measurement precision
If all data blocks are tested to identify bad blocks, then parameter accuracy improves, but testing time and complexity increase
Solution Approach 1:
The patent applies partial action by selectively testing only the minimum necessary number of data blocks rather than all blocks. The control unit determines whether to perform bad block detection based on comparison results, and stops testing a data block as soon as a bad block is identified. This approach achieves sufficient parameter accuracy without the time cost of exhaustive testing of every single block.
Data Source
AI summary
A data storage device and a selecting bad data block method thereof which includes: writing data to a sample block; reading written data of the sample block as read data; comparing the read data and the written data of each data column in sample block, and calculating a number of error bits in each chunk accordingly; selecting a column with the largest number of error bits in a chunk with the largest number of error bits as a bad data column; and recording the sample block as a bad data block when determining that the number of error bits in the chunk is greater than or equal to the first threshold value and the number of bad columns in the chunk is greater than or equal to the second threshold value.