Predictive Coolant Flow Control for Substrate Processing Chambers
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Solution Overview
Problem
Conventional substrate processing systems experience inefficiencies in coolant flow through cooling loops, leading to excessive energy consumption and potential damage from overheating due to unregulated coolant flow.
Innovation Solution
A method and system that utilize a digital twin model to predict cooling parameters based on process recipes, allowing for variable coolant flow rates and temperatures to be adjusted dynamically, thereby optimizing energy use and maintaining optimal processing chamber temperatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If coolant flow is increased to prevent overheating, then temperature control reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts coolant flow rate based on real-time temperature sensor readings and predictive modeling. The flow rate varies according to actual thermal conditions rather than maintaining a constant high flow, allowing the system to maintain reliable temperature control while minimizing energy consumption during low-heat-generation periods.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where temperature sensors continuously monitor processing chamber conditions, and this data feeds into a predictive model that adjusts coolant flow parameters. This feedback loop ensures temperature reliability is maintained while optimizing energy usage by adjusting flow only when and where needed.
2Use of energy by moving object
If coolant flow is regulated dynamically, then energy consumption is reduced, but system complexity increases
Solution Approach 1:
The system introduces a predictive modeling layer as an intermediary between temperature sensing and coolant flow control. This digital twin model processes sensor data and predicts future thermal conditions, enabling proactive adjustments to coolant flow. This intermediary layer reduces the need for complex real-time control algorithms while maintaining energy optimization.
Solution Approach 2:
The predictive model performs preliminary analysis of thermal conditions and anticipates future heating requirements before they occur. By predicting cooling needs in advance, the system can pre-adjust coolant flow parameters, simplifying the control logic compared to reactive high-frequency adjustments and reducing overall system complexity.
3Object-affected harmful factors
If constant high coolant flow is used, then overheating prevention is ensured, but manufacturing throughput decreases due to excessive energy use
Solution Approach 1:
The system continuously adjusts coolant flow parameters (flow rate, temperature, timing) based on predictive modeling of substrate processing thermal characteristics. By dynamically changing these parameters to match actual cooling needs, the system prevents overheating during high-heat processes while reducing flow during low-heat periods, thereby eliminating unnecessary energy consumption that would reduce manufacturing throughput and cost.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces energy consumption by optimizing coolant flow according to predicted cooling needs, prevents overheating and subsequent damage, and enhances manufacturing throughput by maintaining precise temperature control.
Implementation Method 1
coolant flow to remove heat
Implementation Method 2
flow of coolant through a cooling loop
Data Source
AI summary
Technologies directed to cooling flow according to predicted cooling parameters for substrate processing are described. In some embodiments, a method includes receiving first data indicative of a process recipe for processing a substrate in a processing chamber of a substrate processing system. The method further includes inputting the first data into a model. The model includes a digital twin configured to represent thermal characteristics of the processing chamber. The method further includes receiving, via the model, a predicted value of a parameter associated with a flow of coolant through a cooling loop of the processing chamber. The method further includes causing coolant to flow through the cooling loop based on the predicted value of the parameter during execution of the process recipe in the processing chamber.