Processing Chamber Performance Analysis With Integrated Recipe Data
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Solution Overview
Problem
Conventional diagnostic methods for manufacturing equipment are inefficient in analyzing data across multiple stages of processing, leading to suboptimal production consistency and reliability, as they often require human intervention and separate analysis of different data stages, lacking comprehensive integration of recipe, operational, and historical data for real-time corrective actions.
Innovation Solution
A comprehensive analysis module that synthesizes data from various stages of manufacturing, including recipe data, operational data, and historical data, to provide automated recommendations for corrective actions, utilizing rule-based processing, statistical metrics, and machine learning models for improved decision-making and process control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional diagnostic methods are used to analyze manufacturing equipment data, then human intervention and separate analysis of different data stages are required, but this leads to inefficient analysis across multiple stages of processing
Solution Approach 1:
The patent combines recipe data, operational data, and historical data into a single integrated analysis framework. The system merges multiple data sources and analysis stages into one comprehensive diagnostic process, eliminating the need for separate human-led analyses of different data types while improving overall analysis efficiency.
Solution Approach 2:
The system performs automated data synthesis and corrective action identification without requiring human intervention. The diagnostic system serves itself by automatically integrating data from multiple stages, analyzing the combined information, and generating corrective actions, thereby eliminating manual labor while maintaining analysis quality.
2Reliability
If separate analysis of different data stages is performed, then analysis process is simplified, but production consistency and reliability are suboptimal
Solution Approach 1:
The patent merges recipe data, operational data, and historical data into a unified analysis model. This comprehensive integration ensures that all relevant factors affecting production consistency are considered simultaneously, leading to more reliable and consistent production outcomes compared to separate analysis approaches.
Solution Approach 2:
The analysis system is designed to handle multiple data types and serve multiple functions within a single framework. It simultaneously performs recipe validation, operational monitoring, historical trend analysis, and corrective action generation, making the system universally applicable across different stages of manufacturing while improving reliability.
3Ease of operation
If comprehensive integration of recipe, operational, and historical data is implemented, then real-time corrective actions are improved, but data processing complexity increases
Solution Approach 1:
The system automatically synthesizes data from multiple sources and generates corrective actions without human intervention. This self-service capability simplifies operation by eliminating manual data collection and analysis steps, even though the underlying data processing is complex. The system handles the complexity internally while presenting simple, actionable outputs to users.
Solution Approach 2:
The system performs preliminary data synthesis and analysis in real-time, preparing corrective actions before they are needed. By pre-processing and integrating data continuously, the system is ready to provide immediate corrective recommendations when issues arise, making the implementation process easier and faster.
4Extent of automation
If automated data synthesis is performed across multiple stages, then human intervention is reduced, but computational requirements increase
Solution Approach 1:
The system performs automated data synthesis and analysis without human intervention, achieving high extent of automation. The computational processes are executed automatically by the system itself, reducing manual labor while the computational energy consumption is managed through efficient algorithm design and data processing strategies inherent in the automated framework.
Data Source
AI summary
A method includes receiving, by a processing device, first data indicative of a processing recipe. The method further includes receiving second data. The second data includes operational data associated with the processing recipe. The method further includes receiving third data. The third data includes historical data associated with the processing recipe. The method further includes performing analysis indicative of performance of a processing chamber based on the first, second, and third data. The method further includes causing performance of a corrective action in view of the analysis.