Semiconductor Digital Twin Modeling with Stacked Physics and AI
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Solution Overview
Problem
Creating a digital twin of a semiconductor processing environment is time-consuming and resource-intensive, requiring numerous sensors and large datasets, which hinders efficient monitoring and diagnosis of performance anomalies.
Innovation Solution
A method involving the generation of mathematical-based and machine learning-based variables using sensor data, stacked through a meta-learning model to predict performance characteristics, such as material deposit and heater states, reducing the need for extensive resources and data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional digital twin creation methods are used, then the digital twin can accurately replicate the semiconductor processing environment, but the process becomes time-consuming and resource-intensive requiring numerous sensors and large datasets
Solution Approach 1:
The patent segments the digital twin creation process into multiple independent components: physics-based models for thermal and fluid dynamics, machine learning models for material deposit prediction, and sensor data processing modules. This segmentation allows each component to be developed and validated separately, reducing overall creation time while maintaining accuracy.
Solution Approach 2:
The patent implements preliminary action by pre-configuring the digital twin framework with established physics-based models and pre-trained machine learning algorithms before actual semiconductor processing begins. This allows the system to be rapidly deployed without requiring extensive data collection and model training from scratch.
2Reliability
If traditional digital twin creation methods are used, then comprehensive environmental monitoring is achieved, but numerous sensors and large datasets are required making the process resource-intensive
Solution Approach 1:
The patent introduces physics-based models as intermediaries that translate limited sensor measurements into comprehensive environmental predictions. These models act as mediators that infer unmeasured parameters from measured ones, reducing the number of physical sensors needed while maintaining monitoring comprehensiveness.
Solution Approach 2:
The patent replaces physical sensors with virtual sensors implemented through machine learning models. These virtual sensors predict parameters such as material deposit characteristics and heater states without requiring physical measurement devices, thereby reducing hardware complexity while maintaining monitoring reliability.
3Measurement precision
If more sensors and data are collected to improve digital twin accuracy, then better performance characteristic prediction is achieved, but the resource requirements and complexity increase
Solution Approach 1:
The patent changes the fundamental parameters of the monitoring approach by shifting from direct physical measurement to model-based prediction. This parameter change allows accurate performance characteristic prediction using fewer sensors, as the system relies on physics-based relationships and machine learning inferences rather than extensive physical measurements.
Data Source
AI summary
A method of generating a digital twin of an environment includes generating one or more mathematical-based variables based on a mathematical model of the environment and sensor data from one or more sensors of the environment, generating one or more machine learning-based variables based on a machine learning-based model of the environment and the sensor data, and stacking the one or more mathematical-based variables and the one or more machine learning-based variables based on a meta-learning model to generate a machine learning input for predicting a performance characteristic of the environment.