Substrate Support Digital Twin for Fast Chamber Drift Characterization
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Solution Overview
Problem
Conventional methods for characterizing substrate supports in manufacturing chambers are costly, time-consuming, and inefficient, as they rely on empirical experimentation and fail to account for drift in chamber properties over time, leading to inconsistent substrate quality and potential waste.
Innovation Solution
A sensor assembly system is used to measure substrate support properties, which are input into a physics-based digital twin model to predict performance and enable corrective actions, optimizing substrate production without the need for empirical testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical experimentation methods are used to characterize substrate supports, then characterization can be performed, but the process becomes costly, time-consuming, and inefficient
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the substrate support that replicates its physical properties and behavior. This digital model allows for virtual characterization and testing without requiring physical experimentation, thereby reducing time and cost while maintaining characterization accuracy. The digital twin is updated with actual sensor data to maintain fidelity to the physical substrate support.
Solution Approach 2:
The patent replaces physical empirical experimentation with computational modeling and simulation. Instead of conducting time-consuming physical tests on substrate supports, the system uses a physics-based digital twin model to predict performance characteristics, substituting mechanical/physical testing with computational analysis that is faster and more efficient.
2Measurement precision
If empirical experimentation methods are used to characterize substrate supports, then characterization can be performed, but the process becomes costly
Solution Approach 1:
The digital twin serves as a virtual replica that eliminates the need for expensive physical experimentation. By using the digital model to characterize substrate supports, the system avoids costs associated with material consumption, equipment wear, and manual testing procedures, thereby reducing overall characterization costs while maintaining accuracy.
Solution Approach 2:
The patent substitutes expensive physical testing infrastructure and materials with computational resources. The physics-based digital twin model requires only computing power and sensor data, eliminating the need for costly experimental setups, consumable materials, and extensive manual testing procedures.
3Reliability
If conventional characterization methods are used, then substrate support properties can be measured, but drift in chamber properties over time is not accounted for, leading to inconsistent substrate quality
Solution Approach 1:
The system continuously monitors chamber properties using sensors and feeds this data back to update the digital twin model. This feedback mechanism allows the system to detect and account for drift in chamber properties over time, maintaining accurate predictions of substrate quality and enabling real-time adjustments to processing parameters to ensure consistency.
Solution Approach 2:
The digital twin model predicts potential quality issues before they occur by simulating the effects of chamber property drift on substrate processing. This preliminary action allows operators to adjust processing parameters proactively to maintain substrate quality consistency, rather than reacting to quality problems after they occur.
Data Source
AI summary
A method includes receiving data indicative of properties of a substrate support from one or more sensors of a removable sensor assembly disposed proximate to the substrate support. The method further includes providing data based on the data indicative of properties of a substrate support to a physics-based model of the substrate support. The method further includes receiving predicted performance data of the substrate support from the physics-based model.