Fault Detection Feature Extraction in Semiconductor FDC Systems
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Solution Overview
Problem
In semiconductor fabrication, detecting and classifying faults in processing tools is challenging due to reliance on user experience rather than standardized criteria, leading to difficulties in identifying defects during or after the fabrication process.
Innovation Solution
A method involving a computing system that uses sensors to generate profiles of parameters, models these profiles to extract fault-related features, calculates scores based on these features, and displays a ranking to facilitate fault detection and classification, enabling the identification of fault events or errors in semiconductor manufacturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user knowledge and experience are used to identify faults on FDC charts, then fault detection can be performed, but the detection is not standardized and relies on subjective judgment
Solution Approach 1:
The patent replaces the manual, experience-based visual inspection mechanism with an automated image processing and pattern recognition system. The system captures FDC charts, processes them through image analysis algorithms, and automatically identifies fault patterns, eliminating subjective human judgment and providing standardized, precise fault detection.
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the FDC chart display and fault identification. This intermediary layer captures the chart, processes it through standardized algorithms, and outputs structured fault information, bridging the gap between visual data and reliable fault detection.
2Measurement precision
If standardized criteria are implemented for fault detection, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent segments the fault detection process into distinct functional modules: chart capture, image preprocessing, feature extraction, pattern recognition, and result output. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving standardized precise fault identification.
Solution Approach 2:
The system performs self-service by automatically capturing FDC charts, processing them through standardized algorithms, and generating fault identification results without requiring external intervention. This automation reduces operational complexity while maintaining high measurement precision through consistent application of detection criteria.
Data Source
AI summary
A system includes at least one tool, a storage device and a processor. The at least one tool performs semiconductor fabrication processes on at least one wafer, in which the at least one tool includes sensors. The storage device stores computer program codes. The processor executes the computer program codes in the storage device for: modeling profiles from the sensors to generate a modeling result; extracting features from the modeling result corresponding to the modeled profiles; based on the extracted features, extracting scores each representing a degree of the at least one wafer being processed by the at least one tool; and based on the extracted scores, displaying a ranking for fault detection of the at least one wafer.