Clone-Model Thermal Control for Wafer Temperature Setpoint Tracking
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Solution Overview
Problem
Thermal processing systems face challenges in accurately tracking temperature setpoints during semiconductor wafer heating, leading to inefficiencies and inaccuracies in chemical and physical transformations.
Innovation Solution
A control system that uses sensors to determine actual workpiece temperatures, compares them to setpoints, and adjusts heat source parameters through a system model and machine learning algorithms to improve temperature tracking, incorporating submodels for radiometric, optical, and thermal simulations to refine control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional thermal processing control systems are used, then the system structure is simple, but the temperature setpoint tracking precision deteriorates
Solution Approach 1:
The control system is segmented into multiple functional modules: a thermal model module that predicts workpiece temperature based on process parameters, a feedback module that receives actual temperature measurements, and an adjustment module that modifies control parameters. This modular segmentation allows complex temperature tracking control to be achieved through coordinated simple modules, resolving the contradiction between precision and complexity.
Solution Approach 2:
The system performs preliminary temperature prediction using the thermal model before actual heating occurs. By pre-calculating expected temperature trajectories and comparing them with target setpoints, the system can proactively adjust control parameters to ensure accurate tracking, rather than reacting to temperature deviations after they occur.
Solution Approach 3:
The control system implements a closed-loop feedback mechanism where actual workpiece temperature measurements are continuously compared with predicted temperatures from the thermal model. Based on this feedback, the system dynamically adjusts heating parameters to minimize tracking errors, achieving high precision without requiring overly complex open-loop control systems.
2Productivity
If thermal processing is performed to achieve chemical and physical transformations, then the productivity is improved, but the temperature control accuracy deteriorates
Solution Approach 1:
The system dynamically changes control parameters including heating power, heating duration, and lamp intensity based on real-time temperature feedback and thermal model predictions. By continuously optimizing these parameters during the thermal processing cycle, the system maintains high temperature control accuracy even during rapid heating phases required for efficient semiconductor wafer transformation.
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 significantly reduces temperature setpoint tracking errors, enhancing the efficiency and accuracy of thermal processing, leading to more precise workpiece temperature control and improved process productivity.
Implementation Method 1
heating lamps, lasers, or other heat sources
Implementation Method 2
heating lamps
Implementation Method 3
one or more sensors configured to obtain data associated with a workpiece temperature
Data Source
AI summary
A control system operable to train a control tuner to generate temperature setpoint tracking improvements for a thermal processing system is provided. In one example implementation, temperature setpoint tracking improvements are achieved by generating system controller parameter adjustments based on a difference between a simulated workpiece temperature estimate and an actual workpiece temperature estimate. For example, a system model can generate a simulated workpiece temperature estimate simulating an actual workpiece temperature estimate, and based on the difference between the simulated and actual workpiece temperature estimates, generate clone controller parameter adjustments. The clone controller parameter adjustments can be used to generate system controller parameter adjustments, which can improve temperature setpoint tracking for the thermal processing system.