Memory Test Apparatus Block Defect Compression
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Solution Overview
Problem
Conventional test apparatuses for semiconductor devices are inefficient in investigating the cause of yield issues during manufacturing, as they record detailed information for every cell, leading to unnecessary data storage and compression challenges, especially when defects are spread out across blocks, which hampers the analysis process.
Innovation Solution
A test apparatus with a fail information storage section, a counting section, a comparing section, a converting section, and a compressing section that identifies and compresses data based on the number of defective cells in each block, allowing for efficient data management by converting and compressing data only when the number of defective cells exceeds a predetermined reference value, and indicating all cells as defective when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed information for every cell is recorded, then complete defect information is available, but data storage capacity is needlessly filled and compression efficiency decreases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of defect information based on block characteristics. Blocks with few defects (where individual cell positions matter) retain detailed cell-level information, while blocks with many defects (where individual positions are less meaningful) are summarized with aggregate counts. This selective approach preserves necessary information while reducing overall data volume.
Solution Approach 2:
The patent segments the memory device into multiple blocks and processes each block independently with different compression strategies. By dividing the data into block-level segments, the system can apply appropriate information retention levels to each segment based on its defect density, rather than uniformly processing all cell data.
2Loss of information
If detailed information for every cell is recorded, then complete defect information is available, but data compression ratio decreases when defects are spread out
Solution Approach 1:
The patent applies local quality by differentiating the treatment of defect information based on block characteristics. Blocks with few defects (where individual cell positions matter) retain detailed cell-level information, while blocks with many defects (where individual positions are less meaningful) are summarized with aggregate counts. This selective approach preserves necessary information while reducing overall data volume.
Solution Approach 2:
The patent applies partial action by selectively applying detailed recording only where necessary (blocks with few defects) and using summary representation where excessive detail would be wasteful (blocks with many defects). This partial approach to detailed recording optimizes the balance between information completeness and compression efficiency.
3Stability of the object's composition
If uniform recording method is used for all blocks, then consistency is maintained, but efficiency is reduced when blocks have different defect densities
Solution Approach 1:
The patent applies dynamics by making the data recording method adaptive rather than static. The system dynamically selects between detailed cell-level recording and aggregate summary recording based on the actual defect density of each block. This dynamic adjustment optimizes both consistency (through standardized block-level processing) and efficiency (by adapting to varying defect patterns).
Solution Approach 2:
The patent applies local quality by differentiating the treatment of defect information based on block characteristics. Blocks with few defects (where individual cell positions matter) retain detailed cell-level information, while blocks with many defects (where individual positions are less meaningful) are summarized with aggregate counts. This selective approach preserves necessary information while reducing overall data volume.
Data Source
AI summary
A test apparatus includes a test section that executes testing of each cell of the memory under test, a fail information storage section that stores fail information in a fail memory; a counting section that counts the number of defective cells in each block, a reading request receiving section that receives a request to read the fail information of each cell, a comparing section that compares the number of defective cells in a block to a predetermined reference number, a converting section that, in a case where the number of defective cells exceeds the predetermined reference value, converts a plurality of consecutive pieces of fail information in a response data string into a value indicating defectiveness, and a compressing section that compresses the response data string and returns a compressed response data string.


