17719722. SEMICONDUCTOR FABRICATION PROCESS AND METHOD OF OPTIMIZING THE SAME simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
SEMICONDUCTOR FABRICATION PROCESS AND METHOD OF OPTIMIZING THE SAME
Organization Name
Inventor(s)
MINHYEOK Kwon of SUWON-SI (KR)
SHIGENOBU Maeda of SEONGNAM-SI (KR)
SEMICONDUCTOR FABRICATION PROCESS AND METHOD OF OPTIMIZING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 17719722 titled 'SEMICONDUCTOR FABRICATION PROCESS AND METHOD OF OPTIMIZING THE SAME
Simplified Explanation
The abstract of this patent application describes a program code that, when executed by a processor, performs various tasks related to analyzing fabrication data in the semiconductor manufacturing process. Here is a simplified explanation of the abstract:
- The program code inputs fabrication data, which includes multiple parameters associated with the semiconductor fabricating process, to a framework.
- The framework generates a first class for analyzing the fabrication data and extracts a first parameter and a second parameter associated with it from the multiple parameters.
- A second class is then generated as a sub class of the first class, specifically designed for analyzing the first parameter.
- The first parameter and the second parameter are modified and transformed into a data structure suitable for storage in the second class.
- Data analysis is performed on the first parameter and the second parameter.
- The first parameter and the second parameter are further transformed into tensor data.
- Finally, the tensor data is inputted to a machine learning model.
Potential applications of this technology:
- Semiconductor manufacturing process optimization
- Quality control and defect detection in semiconductor fabrication
- Predictive maintenance in semiconductor manufacturing equipment
Problems solved by this technology:
- Complex analysis of fabrication data is automated, reducing manual effort and potential errors.
- Efficient extraction and storage of relevant parameters for analysis.
- Integration of machine learning techniques for improved decision-making in semiconductor manufacturing.
Benefits of this technology:
- Improved efficiency and accuracy in analyzing fabrication data.
- Enhanced understanding of the semiconductor manufacturing process.
- Potential for cost savings and improved product quality in the semiconductor industry.
Original Abstract Submitted
The program code, when executed by a processor, causes the processor to input fabrication data including a plurality of parameters associated with a semiconductor fabricating process to a framework to generate a first class for analyzing the fabrication data, to extract a first parameter targeted for analysis and a second parameter associated with the first parameter from the plurality of parameters and generate a second class for analyzing the first parameter as a sub class of the first class, to modify the first parameter and the second parameter into a data structure having a format appropriate to store in the second class, so as to be stored in the second class, to perform data analysis on the first parameter and the second parameter, to transform the first parameter and the second parameter into corresponding tensor data, and to input the tensor data to the machine learning model.
