18521112. INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM, AND OPTIMAL SOLUTION SEARCH METHOD simplified abstract (Tokyo Electron Limited)
Contents
- 1 INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM, AND OPTIMAL SOLUTION SEARCH METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM, AND OPTIMAL SOLUTION SEARCH METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the apparatus handle outliers in the data when training the machine learning model?
- 1.11 What is the computational complexity of the graph creation process?
- 1.12 Original Abstract Submitted
INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM, AND OPTIMAL SOLUTION SEARCH METHOD
Organization Name
Inventor(s)
Satoshi Itoh of Yamanashi (JP)
Hitoshi Yonemichi of Sapporo City (JP)
INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM, AND OPTIMAL SOLUTION SEARCH METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18521112 titled 'INFORMATION PROCESSING APPARATUS, STORAGE MEDIUM, AND OPTIMAL SOLUTION SEARCH METHOD
Simplified Explanation
The patent application describes an information processing apparatus that uses machine learning to train a relationship between process conditions and processing results of a substrate processing apparatus. The apparatus then infers multiple processing results based on different process conditions, plots them on a graph, and displays information to help an operator select an optimal solution for the process condition.
- Learning trainer trains a machine learning model to understand the relationship between process conditions and processing results.
- Inferrer uses the trained model to predict multiple processing results based on different process conditions.
- Graph creator plots the inferred processing results on a graph with achievement levels for target values.
- Information display shows the graph to help operators choose the best solution for the process condition.
Potential Applications
This technology could be applied in industries such as semiconductor manufacturing, where optimizing process conditions can lead to improved efficiency and quality of the final product.
Problems Solved
This technology helps operators quickly identify the best process conditions for optimal processing results, saving time and resources that would otherwise be spent on trial and error.
Benefits
The apparatus streamlines the decision-making process for operators, leading to improved efficiency, reduced waste, and enhanced overall performance of the substrate processing apparatus.
Potential Commercial Applications
"Optimizing Process Conditions with Machine Learning Graphs" could be used in industries such as electronics manufacturing, pharmaceuticals, and chemical processing to improve process efficiency and product quality.
Possible Prior Art
One possible prior art could be the use of statistical analysis tools to optimize process conditions in manufacturing. However, the specific combination of machine learning, graph plotting, and operator guidance as described in this patent application may be a novel approach.
Unanswered Questions
How does the apparatus handle outliers in the data when training the machine learning model?
The patent application does not provide details on how outliers in the data are addressed during the training of the machine learning model. It would be important to understand how robust the model is to outliers in real-world applications.
What is the computational complexity of the graph creation process?
The patent application does not mention the computational resources required to create and display the graph with multiple inferred processing results. Understanding the computational complexity can help assess the feasibility of implementing this technology in different settings.
Original Abstract Submitted
An information processing apparatus includes: a learning trainer configured to train a machine learning model to train a relationship between a process condition and a processing result of a substrate processing apparatus that has executed a processing based on the process condition; an inferrer configured to infer a plurality of processing results depending on a plurality of process conditions using the trained machine learning model; a graph creator configured to plot the plurality of processing results inferred with the machine learning model on a graph with an achievement level for a plurality of target values of the plurality of processing results as a plurality of axes; and an information display configured to display, on the graph, information used by an operator to select an optimal solution for the process condition, based on the plot of the plurality of inferred processing results.
