Manufacturing Chamber Sensor Summaries for Tool Matching Diagnosis
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Solution Overview
Problem
Conventional systems face inefficiencies in diagnosing and addressing sub-optimal performance in manufacturing equipment due to cumbersome analysis of large amounts of sensor data, leading to wasted time, energy, and unscheduled downtime, as well as the production of sub-optimal products.
Innovation Solution
The implementation of summary data generation using statistical and machine learning methods to process trace sensor data, generating quality index scores and alerts for sub-optimal performance, and utilizing machine learning models for root cause identification and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems analyze large amounts of sensor data to diagnose manufacturing equipment performance, then diagnostic accuracy is improved, but analysis time and computational resources are excessively consumed
Solution Approach 1:
The patent segments the manufacturing equipment into multiple subsystems (e.g., RF subsystem, microwave subsystem, water cooling subsystem, gas delivery subsystem, power subsystem) and analyzes sensor data for each subsystem separately. This segmentation allows the system to focus on specific problem areas rather than processing all sensor data uniformly, thereby reducing overall analysis time while maintaining diagnostic accuracy for each subsystem.
Solution Approach 2:
The patent extracts and prioritizes critical sensor data from the large volume of available sensor data. The system identifies and extracts key performance indicators and anomaly indicators from each subsystem, separating essential diagnostic information from redundant data. This extraction process reduces the data volume requiring detailed analysis while preserving the information necessary for accurate diagnosis.
2Reliability
If conventional systems process all sensor data to identify root causes, then diagnostic completeness is improved, but computational energy and processing power are excessively consumed
Solution Approach 1:
The patent performs preliminary analysis by continuously monitoring sensor data and identifying anomalies before they lead to sub-optimal product production. The system pre-processes sensor data to detect early signs of equipment degradation, prioritizing further analysis only for subsystems showing anomalies. This preliminary action reduces the need for exhaustive computational analysis of all sensor data while maintaining diagnostic completeness for problematic areas.
Solution Approach 2:
The patent applies different levels of analysis depth to different subsystems based on their current state. Subsystems showing normal operation receive minimal processing, while subsystems exhibiting anomalies receive more intensive analysis. This local quality approach ensures computational resources are concentrated where needed, maintaining diagnostic completeness for problematic subsystems while reducing overall energy consumption.
3Productivity
If conventional systems wait for product quality issues to manifest, then production output is maintained, but unscheduled downtime and waste increase
Solution Approach 1:
The patent performs preliminary diagnosis by continuously analyzing sensor data to detect equipment anomalies before they result in sub-optimal product production. The system identifies early warning signs of equipment degradation and alerts operators to potential issues, enabling preventive maintenance scheduling during planned downtime rather than waiting for product quality failures. This preliminary action maintains production output by avoiding unscheduled downtime while preserving equipment reliability.
4Measurement precision
If conventional systems use detailed sensor analysis to identify problematic subsystems, then diagnostic precision is improved, but system complexity and analysis difficulty increase
Solution Approach 1:
The patent divides the complex manufacturing equipment into distinct subsystems with dedicated analysis protocols. Each subsystem (RF, microwave, water cooling, gas delivery, power) is analyzed independently using subsystem-specific sensor data and diagnostic criteria. This segmentation simplifies the overall analysis system by breaking down complex equipment into manageable components, reducing analysis difficulty while maintaining diagnostic precision for each subsystem.
Solution Approach 2:
The patent introduces an intermediary layer that processes and interprets sensor data before presenting diagnostic results to operators. The system acts as an intermediary between raw sensor data and human interpretation, automatically identifying anomalies, prioritizing problematic subsystems, and presenting simplified diagnostic information. This intermediary layer reduces the complexity of the analysis system by handling the computational burden automatically while maintaining high diagnostic precision.
Data Source
AI summary
A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. Generating the summary data includes identifying a steady state and transient portion of the trace sensor data and generating a first and second portion of summary data based on the steady state and transient portions. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.