Substrate Holding Mechanism State Prediction From Operating Conditions
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Solution Overview
Problem
Existing substrate processing apparatuses face challenges in accurately analyzing the impact of operating conditions on the substrate holding mechanism, making it difficult to predict the state of the mechanism effectively.
Innovation Solution
An information processing apparatus that acquires operating condition information, including substrate rotating conditions, holding position, and pressing load, and uses a machine learning model to predict the state of the substrate holding mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple operating condition factors (rotational state, holding position, pressing load) are considered to accurately analyze substrate holding mechanism state, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent combines multiple operating condition factors (rotational state, holding position, pressing load) into a unified machine learning model that processes all inputs simultaneously. This merging approach enables comprehensive analysis of how these factors interact to affect substrate holding mechanism state, achieving accurate predictions without requiring separate analysis systems for each factor.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between operating conditions and substrate holding mechanism state prediction. This intermediary component learns the complex relationships and interactions between multiple operating factors and the mechanism state, simplifying the overall system architecture while maintaining high prediction accuracy.
2Reliability
If machine learning is used to predict substrate holding mechanism state, then prediction capability is established, but computational resources and time are required
Solution Approach 1:
The patent performs preliminary training of the machine learning model using historical operating condition data and corresponding substrate holding mechanism state data. This preliminary action establishes the model's prediction capability in advance, so that during actual operation, the model can quickly predict mechanism state without requiring complex real-time computations, thus reducing operational computational time.
Data Source
AI summary
The information processing apparatus (5) includes an information acquisition section (500) configured to acquire operating condition information including operation conditions of a substrate processing apparatus (2) including a substrate holder (241); and a state prediction section (501) configured to predict substrate-holding-mechanism state information corresponding to the operating condition information by inputting the operating condition information to a learning model that has been generated by machine learning that causes the learning model to learn a correlation between the operating condition information and the substrate-holding-mechanism state information indicating a state of the substrate holding mechanism (241a, 241c, 241e) when the substrate processing device (2) operates under the operating conditions indicated by the operating condition information.