Processing Chamber ML Retraining for Gradual and Sudden Drift
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Solution Overview
Problem
Manufacturing equipment performance changes over time due to gradual or sudden alterations, leading to reduced predictive power of machine learning models, which are costly and inefficient to retrain with conventional methods, resulting in suboptimal operating conditions and increased downtime.
Innovation Solution
A method to determine whether changes in processing chamber conditions are gradual or sudden, employing distinct training processes for each type of change to generate new machine learning models, using a combination of existing and synthetic data to maintain predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional retraining methods are used to maintain predictive accuracy of machine learning models, then model accuracy is preserved, but material and time expenditure increase significantly
Solution Approach 1:
The patent segments the retraining process into two distinct approaches based on the type of change detected: gradual changes use incremental updates to existing models, while sudden changes trigger complete retraining. This segmentation allows the system to apply the appropriate level of retraining effort, avoiding unnecessary full retraining for gradual changes and thus reducing time expenditure while maintaining accuracy when needed.
Solution Approach 2:
The system changes the parameter of retraining intensity based on the detected type of chamber condition change. By monitoring changes in processing chamber conditions and classifying them as gradual or sudden, the system dynamically adjusts the retraining parameter (incremental vs. complete), thereby optimizing the balance between maintaining predictive accuracy and reducing retraining time and resource consumption.
2Measurement precision
If conventional retraining methods are used to maintain predictive accuracy of machine learning models, then model accuracy is preserved, but material consumption increases
Solution Approach 1:
The patent segments the retraining process into two distinct approaches based on the type of change detected: gradual changes use incremental updates to existing models, while sudden changes trigger complete retraining. This segmentation allows the system to apply the appropriate level of retraining effort, avoiding unnecessary full retraining for gradual changes and thus reducing material expenditure while maintaining accuracy when needed.
Solution Approach 2:
The system changes the parameter of retraining intensity based on the detected type of chamber condition change. By monitoring changes in processing chamber conditions and classifying them as gradual or sudden, the system dynamically adjusts the retraining parameter (incremental vs. complete), thereby optimizing the balance between maintaining predictive accuracy and reducing material consumption.
3Productivity
If machine learning models are not retrained, then material and time expenditure are reduced, but predictive power decreases leading to suboptimal operating conditions
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors processing chamber conditions and compares them against the training conditions of the machine learning model. When changes are detected, the system triggers appropriate retraining actions (incremental or complete) to update the model, ensuring predictive power is maintained. This feedback loop optimizes operating efficiency by keeping models accurate without requiring constant retraining, thus balancing productivity and reliability.
4Reliability
If frequent retraining is performed to maintain model accuracy, then predictive power is maintained, but equipment downtime increases
Solution Approach 1:
The patent segments the retraining process into two distinct approaches based on the type of change detected: gradual changes use incremental updates to existing models, while sudden changes trigger complete retraining. This segmentation allows the system to apply the appropriate level of retraining effort, avoiding unnecessary full retraining for gradual changes and thus reducing equipment downtime while maintaining predictive accuracy when needed.
Solution Approach 2:
The system changes the parameter of retraining intensity based on the detected type of chamber condition change. By monitoring changes in processing chamber conditions and classifying them as gradual or sudden, the system dynamically adjusts the retraining parameter (incremental vs. complete), thereby optimizing the balance between maintaining predictive accuracy and minimizing equipment downtime.
Data Source
AI summary
A method includes determining that conditions of a processing chamber have changed since a trained machine learning model associated with the processing chamber was trained. The method further includes determining whether a change in the conditions of the processing chamber is a gradual change or a sudden change. Responsive to determining that the change in the conditions of the processing chamber is a gradual change, the method further includes performing a first training process to generate a new machine learning model. Responsive to determining that the change in the conditions of the processing chamber is a sudden change, the method further includes performing a second training process to generate the new machine learning model. The first training process is different from the second training process.