Process Tool Fault Diagnosis Using Event Sequence Analysis
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Solution Overview
Problem
Identifying the root cause of failures in manufacturing systems, particularly in process tools, is time-consuming and resource-intensive due to the complexity of potential causes, often requiring manual review by field engineers and multiple iterations of testing.
Innovation Solution
Implementing autonomous root cause diagnostics through a manufacturing system that analyzes process runs using event sequences, machine learning models, and predefined fault patterns to identify issues and provide corrective actions without additional user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review by field engineers is used to identify root causes, then diagnostic accuracy can be maintained through expert analysis, but the time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs autonomous root cause diagnostics by automatically analyzing event sequences and comparing them against fault patterns, eliminating the need for manual field engineer intervention. The manufacturing system serves itself by identifying issues and providing corrective actions autonomously, thereby reducing time consumption while maintaining diagnostic accuracy through systematic automated analysis.
Solution Approach 2:
The patent replaces the mechanical process of manual review by field engineers with an automated computational system that uses event sequence analysis and fault pattern matching. This substitution transforms the diagnostic process from human-centric manual analysis to machine-driven automated diagnostics, significantly reducing time requirements while preserving diagnostic capability.
2Reliability
If multiple iterations of testing are performed to identify root causes, then diagnostic reliability improves through thorough verification, but productivity decreases due to repeated testing cycles
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring and storing event sequences during normal operation, and by pre-establishing fault patterns and their associations. When a failure occurs, the system can immediately compare the stored event sequence against the pre-defined fault patterns, eliminating the need for multiple iterative testing cycles while maintaining high diagnostic reliability through thorough preliminary data collection and analysis framework setup.
3Measurement precision
If comprehensive analysis of all potential causes is conducted, then measurement precision of root cause identification improves, but device complexity increases due to the need to analyze multiple potential issues
Solution Approach 1:
The patent segments the complex diagnostic problem into manageable components: event sequence collection, event sequence analysis, fault pattern matching, and corrective action identification. By dividing the comprehensive analysis into these discrete segments, the system can systematically evaluate all potential causes without overwhelming complexity, maintaining high root cause identification accuracy through structured modular analysis.
Data Source
AI summary
A method includes obtaining an event sequence related to a set of runs performed by a process tool that has failed, determining, using the event sequence, an issue causing a failure of the process tool, identifying, based on the issue, a first subset of runs from the set of runs and a second subset of runs from the set of runs, identifying a corrective action to address the issue that caused the failure, and causing the corrective action to be provided.