17732412. COMPUTING DEVICE FOR PREDICTING DATA FOR TRANSISTOR MODELING, TRANSISTOR MODELING APPARATUS HAVING THE SAME, AND OPERATING METHOD THEREOF simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 COMPUTING DEVICE FOR PREDICTING DATA FOR TRANSISTOR MODELING, TRANSISTOR MODELING APPARATUS HAVING THE SAME, AND OPERATING METHOD THEREOF
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPUTING DEVICE FOR PREDICTING DATA FOR TRANSISTOR MODELING, TRANSISTOR MODELING APPARATUS HAVING THE SAME, AND OPERATING METHOD THEREOF - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
COMPUTING DEVICE FOR PREDICTING DATA FOR TRANSISTOR MODELING, TRANSISTOR MODELING APPARATUS HAVING THE SAME, AND OPERATING METHOD THEREOF
Organization Name
Inventor(s)
Seyoung Park of Hwaseong-si (KR)
Gwangnae Gil of Yongin-si (KR)
Jonghyun Lee of Hwaseong-si (KR)
COMPUTING DEVICE FOR PREDICTING DATA FOR TRANSISTOR MODELING, TRANSISTOR MODELING APPARATUS HAVING THE SAME, AND OPERATING METHOD THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 17732412 titled 'COMPUTING DEVICE FOR PREDICTING DATA FOR TRANSISTOR MODELING, TRANSISTOR MODELING APPARATUS HAVING THE SAME, AND OPERATING METHOD THEREOF
Simplified Explanation
The patent application describes a method for operating a transistor modeling apparatus using machine learning techniques. The method involves acquiring sample data and first electrical test (ET) data of a transistor mass production stage through a test device. Machine learning is then performed on this data to generate second ET data for the transistor modeling. A representative value is set for the transistor modeling among the second ET data.
- The method involves acquiring sample data and first electrical test (ET) data of a transistor mass production stage.
- Machine learning techniques are applied to the sample data and first ET data to generate second ET data for transistor modeling.
- A representative value is set for the transistor modeling among the second ET data.
Potential Applications
This technology can be applied in various industries and fields where transistor modeling is required, such as:
- Semiconductor manufacturing: The method can be used to improve the accuracy and efficiency of transistor modeling in the production of semiconductors.
- Electronics design: Transistor modeling is crucial in the design of electronic circuits, and this method can enhance the modeling process.
- Research and development: The method can aid in the development of new transistor technologies and improve the understanding of transistor behavior.
Problems Solved
The method addresses several problems associated with transistor modeling:
- Inaccurate modeling: Traditional methods of transistor modeling may not capture all the nuances and variations in transistor behavior, leading to inaccuracies in the modeling process.
- Time-consuming and costly testing: Conventional transistor modeling often requires extensive testing and data collection, which can be time-consuming and expensive.
- Lack of representative values: Without a representative value for transistor modeling, it can be challenging to make accurate predictions and optimizations.
Benefits
The method offers several benefits for transistor modeling:
- Improved accuracy: By incorporating machine learning techniques, the method can enhance the accuracy of transistor modeling, leading to more reliable predictions and optimizations.
- Time and cost savings: The use of machine learning reduces the need for extensive testing and data collection, resulting in time and cost savings in the transistor modeling process.
- Representative value selection: Setting a representative value among the second ET data helps in simplifying the modeling process and making it more practical for real-world applications.
Original Abstract Submitted
A method of operating a transistor modeling apparatus includes acquiring sample data corresponding to transistor modeling through a test device; performing machine learning on the sample data and first electrical test (ET) data of a transistor mass production stage; generating second ET data for the transistor modeling as a result of performing the machine learning; and setting a representative value for the transistor modeling among the second ET data.
