Fractional-Order State Detection for Semiconductor Equipment
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Solution Overview
Problem
Existing state detection systems for industrial machines, such as semiconductor manufacturing apparatuses, face challenges in accurately detecting operational states due to complex relationships between input and output data, especially in systems with multiple operation modes or dynamic movements.
Innovation Solution
A state detection apparatus that acquires input data and observed data from a target apparatus, determines parameters for an estimation model using non-integer order differential equations, and detects the apparatus's state based on these parameters and a pre-generated state detection model through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional state detection systems are used for industrial machines, then the system structure is simple, but the detection accuracy deteriorates due to complex relationships between input and output data
Solution Approach 1:
The patent transforms the complex state detection problem into a parameter estimation problem by changing the approach from directly detecting states to estimating model parameters (mass, damping coefficient, stiffness) that govern the system behavior. This parameter transformation enables accurate state detection through physics-based models while maintaining computational efficiency.
Solution Approach 2:
The patent introduces a physics-based estimation model as an intermediary between input data and state detection. This model acts as a mediator that processes vibration data through established physical laws to infer system states, bridging the gap between raw measurements and meaningful state information without requiring complex machine learning architectures.
2Measurement precision
If complex machine learning models are used to detect states, then detection accuracy improves, but the detection time and computational resources increase
Solution Approach 1:
The patent replaces complex computational machine learning models with a physics-based estimation model that uses established mechanical principles. This substitution leverages domain knowledge of vibration mechanics to achieve accurate state detection through computationally efficient calculations based on physical laws rather than data-intensive neural networks.
Solution Approach 2:
The patent performs preliminary parameter estimation using physics-based models before final state determination. By pre-processing vibration data through established physical relationships and extracting meaningful parameters early in the detection process, the system reduces the computational burden of subsequent state analysis and enables faster overall detection.
3Measurement precision
If standard differential equations are used for modeling, then the model structure is simple, but the accuracy deteriorates for systems with fractional dynamics
Solution Approach 1:
The patent extends traditional integer-order differential equations to fractional-order differential equations by introducing fractional derivatives. This parameter change in the mathematical model allows accurate representation of systems with memory effects and fractional dynamics while maintaining a relatively simple model structure that can be solved using established numerical methods.
Data Source
AI summary
Provided is a state detection apparatus, a state detection method, a generation method of a learning model, and a recording medium, which can be expected to accurately detect a state of a target apparatus such as a semiconductor manufacturing apparatus. The state detection apparatus according to the present embodiment includes: a first acquirer that acquires input data of a target apparatus and observed data of an operation of the target apparatus; a determinator that determines parameters of an estimation model that estimates the observed data from the input data based on the input data and the observed data acquired by the first acquirer; and a detector that detects a state of the target apparatus based on the parameters determined by the determinator.