Cooling flow in substrate processing according to predicted cooling parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional substrate processing systems expend excess energy due to unregulated coolant flow, leading to inefficient heat removal and potential component damage from excessive cooling.
Innovation Solution
Implement a system that predicts coolant flow parameters using a digital twin and machine learning models to regulate coolant flow based on process recipes, optimizing energy consumption and component temperature management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If unregulated coolant flow is used to remove heat from the processing chamber, then heat removal is achieved, but excess energy is consumed
Solution Approach 1:
The coolant flow rate is made dynamic and adjustable based on real-time thermal conditions. The system continuously monitors temperature sensors and adjusts the coolant flow rate accordingly, transitioning from a static fixed flow rate to a dynamic adaptive flow rate that matches the actual cooling requirements of the processing chamber.
Solution Approach 2:
The system implements a feedback control mechanism where temperature sensors monitor the thermal state of the processing chamber and feed this information back to the control system. The control system then adjusts the coolant flow rate based on this feedback, creating a closed-loop control system that optimizes energy consumption while ensuring adequate cooling.
2Reliability
If high coolant flow rate is used to ensure adequate cooling, then component protection is improved, but energy waste increases
Solution Approach 1:
The system changes the operating parameters of the coolant flow based on thermal conditions. By adjusting the flow rate parameter dynamically according to temperature readings and process requirements, the system optimizes the balance between adequate cooling and energy consumption, avoiding both over-cooling and under-cooling scenarios.
Solution Approach 2:
The control system proactively adjusts coolant flow rates based on predicted thermal conditions and process requirements. By anticipating cooling needs before they become critical, the system can optimize energy consumption while ensuring components remain within safe temperature ranges, rather than reacting to overheating conditions.
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
The system conserves energy by adjusting coolant flow rates and temperatures according to predicted parameters, maintaining optimal processing chamber temperatures, reducing energy waste, and preventing component failure.
Implementation Method 1
The model includes a digital twin configured to represent thermal characteristics of the processing chamber
Implementation Method 2
The processing chamber includes a cooling loop for coolant flow to remove heat
Data Source
AI summary
A method includes inputting data associated with a process recipe into a model representative of thermal characteristics of a processing chamber. The method further includes receiving, via the model, predicted data associated with a flow of coolant for cooling the processing chamber. The method further includes causing cooling of the processing chamber based on the predicted data.