Optical System Thermal Fault Diagnosis With Virtual Sensor Modeling
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Solution Overview
Problem
In microlithography projection exposure apparatuses, thermal deformations caused by electromagnetic radiation and heat dissipation lead to optical aberrations, making it difficult to timely identify errors and implement countermeasures due to limited temperature sensors and complex optical systems, resulting in delayed maintenance and reduced availability.
Innovation Solution
A method that uses sensor-assisted measurements of physical variables to model-based determination of parameters at unsensorable positions, enabling more reliable and timely error identification and countermeasure planning, including localization of error causes and thermal load assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature sensors are installed at all component positions to monitor thermal state, then measurement precision and reliability of error identification improve, but device complexity and cost increase
Solution Approach 1:
A thermal model acts as an intermediary between the limited physical sensors and the unmonitored components. The model uses sensor data from accessible positions to infer thermal states at positions where direct sensing is impractical, effectively mediating the information gap without requiring physical sensors at every location.
Solution Approach 2:
The thermal model creates a virtual copy of the thermal field distribution across the optical system. Instead of physically measuring every point, the model replicates thermal behavior through computational simulation, allowing indirect observation of thermal states at unsensorable positions based on measurements from sensor positions.
2Reliability
If comprehensive sensor coverage is implemented to identify errors timely, then reliability of operation improves, but device complexity increases
Solution Approach 1:
The system implements feedback through the thermal model, which continuously compares predicted thermal states with actual sensor measurements. This feedback loop enables the model to adapt and improve its accuracy over time, providing reliable error identification without requiring increased sensor complexity.
Solution Approach 2:
Physical sensor networks are replaced with a computational thermal model that uses mathematical algorithms to predict thermal behavior. This substitution transitions from a mechanical sensing approach to a computational one, reducing physical complexity while maintaining or improving monitoring capability.
3Loss of information
If model-based determination is used to infer parameters at unsensorable positions, then information density and error identification capability improve, but calculation complexity increases
Solution Approach 1:
The thermal model transforms the problem from measuring multiple physical parameters at many locations to measuring fewer parameters and computationally deriving the rest. By changing the approach from direct measurement to parameter transformation through modeling, information density increases without proportional increases in measurement 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
This approach enhances error identification and countermeasure planning reliability and timeliness by increasing information density, allowing for proactive maintenance and reducing unscheduled outages in microlithographic optical systems.
Implementation Method 1
thermal influences (which include both the electromagnetic radiation acting during operation and heat dissipation from components)
Implementation Method 2
Thermally induced deformations of the sensor frame may occur on account of thermal influences
Implementation Method 3
Thermally induced deformations of the sensor frame may occur
Data Source
AI summary
A method for operating an optical system comprises the following steps: (a) using sensors to measure values of at least one physical quantity at a plurality of different sensor positions in the optical system; and (b) diagnosing an existing or expected malfunction of the optical system on the basis of this measurement. The values measured in step (a) are used to perform model-based determination of at least one parameter at other positions, none of which correspond to the sensor positions. The diagnosis in step (b) also being carried out on the basis of this model-based determination.