Multi-Device Diagnostic System Using Hierarchical Failure Grouping
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Solution Overview
Problem
Current automated testing methods for diagnosing defects in mass-produced electronic devices are time-consuming, labor-intensive, and resource-intensive, and struggle to efficiently diagnose multiple devices due to increasing design complexity and technology nodes, leading to significant memory and run-time requirements.
Innovation Solution
A multiple device diagnostic system and method that involves selecting electronic devices based on criteria, using a diagnosis engine with a failure database to group failures into levels for efficient diagnosis, allowing for parallel testing and reduced run-time by classifying devices with similar failing patterns and pooling them for simultaneous diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated testing is performed on multiple devices to detect flaws, then diagnosis accuracy is improved, but memory footprint and run time increase significantly
Solution Approach 1:
The patent segments the diagnosis process into multiple levels: Level 1 groups devices by device type, Level 2 groups by failure pattern, and Level 3 performs detailed diagnosis only on specific subsets. This hierarchical segmentation allows accurate multi-device diagnosis while reducing overall computation time by avoiding full detailed diagnosis on all devices.
Solution Approach 2:
The patent performs preliminary grouping and classification of devices into failure patterns before conducting detailed diagnosis. By pre-organizing devices based on their failure characteristics and device types, the system reduces the scope of detailed analysis needed, thereby reducing run time while maintaining diagnosis accuracy.
2Measurement precision
If automated testing is performed on multiple devices to detect flaws, then diagnosis accuracy is improved, but memory footprint and run time increase significantly
Solution Approach 1:
The patent segments diagnosis data into hierarchical levels, storing only essential grouping information at Level 1 and Level 2, and detailed diagnosis data only for specific subsets at Level 3. This segmentation reduces memory footprint by avoiding the need to load and process all detailed diagnosis data simultaneously while maintaining the ability to perform accurate diagnoses.
Solution Approach 2:
The patent extracts and stores only the essential failure pattern characteristics and device type information at higher levels, removing the need to retain all detailed diagnosis data in memory. This extraction approach maintains diagnosis accuracy by preserving key diagnostic information while significantly reducing memory requirements.
3Adaptability or versatility
If design sizes and technology nodes are increased to improve device capabilities, then device performance is improved, but the number of diagnosis jobs that can be performed decreases
Solution Approach 1:
The patent segments devices into groups based on device type and failure pattern, allowing the diagnosis system to process multiple devices simultaneously at Level 1 and Level 2. This segmentation enables the system to handle increased device complexity while maintaining high productivity by processing multiple devices in parallel through the hierarchical structure.
Solution Approach 2:
The patent creates a universal diagnosis framework that can handle multiple device types and technology nodes through a common hierarchical structure. The Level 1 and Level 2 grouping mechanisms provide multi-functional capability to accommodate various device complexities, allowing the system to perform diagnosis on diverse devices with increased capabilities without reducing productivity.
4Measurement precision
If individual device testing is performed to ensure accurate defect diagnosis, then diagnosis accuracy is improved, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent segments the testing process into hierarchical levels where Level 1 and Level 2 perform efficient grouping and classification, and Level 3 performs detailed individual diagnosis only when necessary. This segmentation maintains defect diagnosis accuracy by performing detailed analysis on specific devices while using efficient grouping for the broader population, thereby improving overall testing efficiency.
Solution Approach 2:
The patent applies partial detailed diagnosis action only to specific device subsets identified at higher levels, rather than performing full individual testing on all devices. This partial action approach maintains diagnosis accuracy for critical cases while significantly improving productivity by avoiding redundant detailed testing on devices that can be accurately classified through grouping.
Data Source
AI summary
Systems and methods for multiple device diagnostics are disclosed herein. Exemplary embodiments provide for a multiple device diagnostic system having a plurality of electronic devices selected for diagnosis based on at least one selection criterion, a diagnosis engine in data communication with a failure database, and a diagnosis results database in data communication with the diagnosis engine. Embodiments further provide that the failure database contains grouped failure data from at least one previously diagnosed electronic device, that the wherein the processor diagnoses defects in one or more of the plurality of electronic devices using the grouped failure data, and that the processor outputs the diagnosis results to the diagnosis results database.