Machine Learning Fabric Concept Drift Mitigation
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Solution Overview
Problem
Concept drift in machine learning models used for predictive analytics and monitoring in lithographic systems reduces their performance over time due to changes in the lithographic apparatus and metrology tools, making them obsolete as the characteristics and properties of the system move away from those the model was trained on.
Innovation Solution
A method and apparatus for adapting a distribution model in a machine learning fabric to mitigate concept drift by using distribution monitoring components to determine a metric representing the correspondence between new data points and previously trained data points, adapting the output based on this metric, and optionally generating and training new distribution monitoring components when the metric exceeds a threshold, allowing the model to self-adjust and maintain performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used for predictive analytics and monitoring in lithographic systems, then performance can be improved, but the models become obsolete over time due to concept drift
Solution Approach 1:
The patent implements dynamic adaptation of the machine learning model by continuously monitoring data distributions and updating the model parameters in response to detected concept drift. The system transitions from a static model to a dynamic one that automatically adjusts to changing lithographic system characteristics, thereby maintaining performance without obsolescence.
Solution Approach 2:
The patent employs feedback mechanisms through distribution monitoring components that continuously evaluate the correspondence between new data points and training data. When concept drift is detected, the feedback loop triggers model adaptation using historical data, ensuring the model remains accurate and up-to-date with current system conditions.
2Productivity
If the lithographic apparatus and metrology tools change over time, then improved performance can be achieved, but concept drift occurs reducing model accuracy
Solution Approach 1:
The patent applies preliminary action by pre-training distribution monitoring components on historical data before concept drift occurs. These components are prepared in advance to detect changes in data distribution, enabling the system to anticipate and respond to concept drift before it significantly impacts model accuracy, thus maintaining reliability alongside system improvements.
Solution Approach 2:
The patent utilizes parameter changes by adjusting model parameters dynamically based on detected concept drift. The distribution monitoring components evaluate statistical parameters of data distributions, and when changes exceed thresholds, the model parameters are updated accordingly. This allows the model to adapt to changing lithographic system parameters while maintaining accuracy.
3Reliability
If distribution monitoring components are continuously trained on new data, then concept drift can be mitigated, but computational resources and time are consumed
Solution Approach 1:
The patent applies partial action by training distribution monitoring components on a selective subset of historical data rather than all available data. The system determines appropriate training data based on detected drift patterns and only trains on relevant portions, reducing computational time and resources while still achieving effective concept drift mitigation.
Solution Approach 2:
The patent employs a more efficient approach by using lightweight distribution monitoring components that can be quickly trained and deployed. Rather than maintaining heavy, permanent training processes, the system uses simpler components that are trained as needed and can be rapidly replaced or updated, reducing the time and computational cost associated with continuous model adaptation.
Data Source
AI summary
Method and apparatus for adapting a distribution model of a machine learning fabric. The distribution model is for mitigating the effect of concept drift, and is configured to provide an output as input to a functional model of the machine learning fabric. The functional model is for performing a machine learning task. The method may include obtaining a first data point, and providing the first data point as input to one or more distribution monitoring components of the distribution model. The one or more distribution monitoring components have been trained on a plurality of further data points. A metric representing a correspondence between the first data point and the plurality of further data points is determined, by at least one of the one or more distribution monitoring components. Based on the error metric, the output of the distribution model is adapted.