18542562. DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING simplified abstract (Applied Materials, Inc.)
Contents
- 1 DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING - 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 Original Abstract Submitted
DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING
Organization Name
Inventor(s)
Thomas Ho Fai Li of Santa Clara CA (US)
Xiaoer Hu of Santa Clara CA (US)
Imaan Seraphine Islam Rahim of Campbell CA (US)
DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18542562 titled 'DETERMINING EQUIPMENT CONSTANT UPDATES BY MACHINE LEARNING
Simplified Explanation
The abstract describes a method involving a trained machine learning model to determine recommended adjustments to equipment constants in a substrate manufacturing system.
- Trained machine learning model used for recommending adjustments to equipment constants in substrate manufacturing system
- First input data provided to model indicating state of manufacturing system
- Second input data provided to model indicating performed adjustment to equipment constant
- Retraining of model based on the difference between recommended and performed adjustments
Potential Applications
This technology could be applied in various industries where equipment constants need to be adjusted in manufacturing processes, such as semiconductor manufacturing, pharmaceutical production, and chemical processing.
Problems Solved
This technology helps in optimizing equipment constants in substrate manufacturing systems, leading to improved efficiency, reduced downtime, and enhanced product quality.
Benefits
The benefits of this technology include increased productivity, cost savings, improved product consistency, and reduced manual intervention in equipment adjustments.
Potential Commercial Applications
A potential commercial application of this technology could be in the semiconductor industry for optimizing equipment constants in the fabrication of silicon wafers.
Possible Prior Art
One possible prior art could be the use of traditional control systems or manual adjustment methods in substrate manufacturing systems before the introduction of machine learning models for equipment constant optimization.
Unanswered Questions
How does the retraining process of the machine learning model work in this method?
The retraining process involves updating the model based on the difference between the recommended adjustment and the performed adjustment to the equipment constant. This helps the model learn from its previous recommendations and improve its accuracy over time.
What types of equipment constants can be adjusted using this method in a substrate manufacturing system?
This method can be used to adjust various equipment constants such as temperature, pressure, flow rates, and other parameters that affect the manufacturing process in a substrate manufacturing system.
Original Abstract Submitted
A method includes providing, to a trained machine learning model configured to determine a recommended adjustment to an equipment constant of a substrate manufacturing system, first input data indicative of a state of the substrate manufacturing system. The method further includes providing, to the trained machine learning model as second input data, an indication of a performed adjustment to the equipment constant. The method further includes retraining the trained machine learning model based on a difference between the recommended adjustment to the equipment constant and the performed adjustment to the equipment constant to generate a retrained machine learning model.
