Model-Based Process Recipe Transfer for Chamber Matching
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Solution Overview
Problem
Generating and maintaining process recipes for multiple manufacturing chambers is costly and inefficient, as conventional methods require significant time, expertise, and resources, especially when transferring recipes between chambers of different designs or components.
Innovation Solution
Utilizing physics-based and data-based models to transfer process recipes between chambers, including machine learning models that predict process parameters to match target performance metrics, reducing the need for iterative adjustments and expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to transfer process recipes between chambers, then recipe transfer can be achieved, but significant time, expertise, and resources are required
Solution Approach 1:
The system performs preliminary actions by training machine learning models in advance using historical process data from multiple chambers. These pre-trained models can then quickly predict optimal process parameters for recipe transfer without requiring iterative adjustments during actual transfer operations, significantly reducing the time and expertise needed.
Solution Approach 2:
Machine learning models serve as intermediaries between source and target chambers during recipe transfer. Instead of direct iterative adjustments between chambers, the ML models mediate the transfer process by learning from historical data and predicting optimal parameters, reducing the need for manual intervention and extensive testing.
2Manufacturing precision
If expert intervention is used for recipe transfer, then accurate process parameters can be obtained, but costs and resource requirements increase
Solution Approach 1:
The system enables self-service by allowing the machine learning models to automatically predict optimal process parameters for recipe transfer without requiring expert intervention. The models learn from historical data and autonomously generate accurate parameter predictions, reducing dependency on expert knowledge while maintaining precision.
Solution Approach 2:
The patent replaces the mechanical system of expert human intervention with an automated machine learning-based system. Instead of relying on human experts to manually adjust and determine process parameters, the ML models automatically perform these functions by learning from historical data, thereby reducing costs and resource requirements while maintaining or improving accuracy.
3Reliability
If iterative adjustments are performed for chamber matching, then performance alignment can be achieved, but the process becomes costly and inefficient
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical process data from multiple chambers before actual chamber matching is needed. This advance preparation enables the models to quickly and accurately predict optimal parameters for matching chamber performance without requiring time-consuming iterative adjustments, thereby improving both reliability and productivity.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning models continuously learn from historical process data and outcomes across multiple chambers. This feedback loop enables the models to improve their predictions over time, achieving reliable chamber performance consistency more efficiently by leveraging accumulated knowledge rather than relying solely on iterative adjustments for each matching scenario.
Data Source
AI summary
A method includes obtaining first output from a first model. The first output is associated with a first process chamber. The first output includes one or more target performance metrics for the first process chamber. The method further includes providing the one or more target performance metrics as input to a second model. The second model is associated with a second process chamber. The method further includes obtaining second output from the second model. The second output includes first process parameters in association with the second process chamber. The first process parameters are predicted to correspond with the one or more target performance metrics. The method further includes performing a corrective action in view of the second output.