Substrate Stage Thermal Parameter Estimation for Temperature Uniformity
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Solution Overview
Problem
Existing substrate processing systems face challenges in accurately estimating parameters such as heat input and thermal resistance in the electrostatic chuck, which affects the uniformity of substrate temperature and processing quality.
Innovation Solution
A parameter estimation system that acquires temperature time-series data from the substrate placement stage, calculates temperature transitions using a physical model, and estimates parameters like heat input and thermal resistance by minimizing the error between measured and modeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If temperature measurement and adjustment functions are implemented in substrate processing apparatus, then substrate temperature control is improved, but parameter estimation accuracy deteriorates due to inability to accurately estimate heat input and thermal resistance
Solution Approach 1:
The system implements feedback by continuously measuring temperature with temperature sensors during substrate processing, comparing measured values with values calculated from the thermal model, and using the error between them to iteratively update and refine the model parameters (heat input, thermal resistance) through optimization algorithms, thereby improving parameter estimation accuracy
Solution Approach 2:
The system changes physical parameters by estimating and updating thermal model parameters (heat input Q, thermal resistance R) based on temperature measurement data, allowing the model to adapt to actual processing conditions and improve accuracy of temperature control predictions
2Manufacturing precision
If physical model parameters are estimated using temperature data, then manufacturing precision is improved, but device complexity increases due to additional measurement and calculation systems
Solution Approach 1:
The system achieves multi-functionality by using the same temperature measurement infrastructure for both real-time temperature control and parameter estimation purposes, allowing the thermal model to serve dual functions of process control and system characterization without requiring separate dedicated measurement systems
Solution Approach 2:
The system implements self-service by using its own temperature measurement data and thermal model to automatically estimate and update its own parameters (heat input, thermal resistance) without requiring external calibration equipment or separate characterization processes, thereby improving manufacturing precision while minimizing additional system complexity
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 effectively optimizes the physical model by accurately estimating key parameters, thereby improving the uniformity of substrate temperature and enhancing the overall processing quality in substrate processing systems.
Implementation Method 1
a cooling base that adjusts the temperature of the substrate placement stage via a cooling layer
Implementation Method 2
an acquisition unit that acquires temperature time-series data obtained by measuring a temperature of a substrate placement stage
Data Source
AI summary
A parameter estimation system includes: an acquisition unit that acquires temperature time-series data obtained by measuring a temperature of a substrate placement stage provided in a substrate processing apparatus in a time-series manner when increasing the temperature of the substrate placement stage, the substrate processing apparatus including the substrate placement stage and a cooling base that adjusts the temperature of the substrate placement stage via a cooling layer; a model calculation unit that calculates a temperature transition of the substrate placement stage using a physical model; an error calculation unit that calculates an error between the acquired temperature time-series data and temperature transition data; and an estimation unit that estimates parameters in the physical model, including a value of heat input to the substrate placement stage and a value of thermal resistance of the cooling layer, based on the calculated error.