Heater System State Modeling for Robust Thermal Control
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Solution Overview
Problem
Industrial thermal systems face challenges in accurately controlling temperature and process variables due to external factors beyond their control, such as wafer type, gases, and pressure differentials, which can lead to unreliable data and operational inefficiencies.
Innovation Solution
A method involving a process control system that generates output controls for a heater system based on intermediate data, associating it with correlation data to create models that predict the state of the heater system, allowing for corrective actions such as power control or alerts, using a combination of mathematical and machine learning models to account for internal and external influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PID control is used to monitor process variables, then the system can maintain basic temperature control, but the control accuracy deteriorates due to external factors beyond system control (wafer type, gases, pressure differentials)
Solution Approach 1:
The system implements feedback by continuously monitoring intermediate data from the PID controller and using it to update the process model. The model compares predicted process variables with actual measurements, and the discrepancies are fed back to refine the model parameters, enabling the system to adapt to external variations and maintain accurate control.
Solution Approach 2:
The system dynamically changes model parameters based on intermediate data. By updating the process model with real-time information about system behavior under different external conditions (different wafer types, gas flows, pressure differentials), the controller adapts its parameters to maintain optimal performance across varying operating conditions.
2Loss of information
If more sensors are added to monitor external factors (wafer type, gases, pressure), then measurement completeness improves, but system complexity and cost increase
Solution Approach 1:
The system uses intermediate data from the existing PID controller as a mediator to infer external factors. Instead of directly measuring all external parameters (wafer type, gas composition, pressure differentials), the model uses the PID controller's intermediate calculations and process variable responses as indirect indicators, reducing the need for additional sensors while still capturing the effects of external variations.
Solution Approach 2:
The system replaces physical sensors with a computational model that processes existing control data. Rather than adding mechanical sensing devices for each external factor, the patent uses software-based modeling and data analysis to extract information about external conditions from the control system's existing data streams, substituting computational complexity for physical complexity.
3Measurement precision
If a complex model incorporating all external factors is created, then prediction accuracy improves, but processing time and computational load increase
Solution Approach 1:
The system applies partial action by focusing the complex modeling effort on the most critical aspects of system behavior. Rather than attempting to model every possible external factor with equal detail, the approach concentrates computational resources on the dominant influences and key process variables, achieving sufficient prediction accuracy without the full computational burden of a complete model.
Data Source
AI summary
A method of controlling a thermal system of an industrial process includes monitoring intermediate data, associating the intermediate data with correlation data, wherein the correlation data includes an internal process control input, an external heater control input, the output control, or a combination thereof. The method further includes generating a model that defines a relationship between the intermediate data and the correlation data, identifying a state of the heater system based on the model, and selectively performing a corrective action based on the identified state of the heater system.