17741860. METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE AND COMPUTING DEVICE PERFORMING THE SAME simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE AND COMPUTING DEVICE PERFORMING THE SAME
Organization Name
Inventor(s)
Jonghyun Lee of Hwaseong-si (KR)
Gwangnae Gil of Yongin-si (KR)
Seyoung Park of Hwaseong-si (KR)
METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE AND COMPUTING DEVICE PERFORMING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 17741860 titled 'METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE AND COMPUTING DEVICE PERFORMING THE SAME
Simplified Explanation
The patent application describes a method for predicting characteristics of a semiconductor device using simulation and deep learning techniques.
- The method involves generating a simulation current-voltage curve of the semiconductor device using compact models.
- Compact models simulate the device data to generate simulation result data indicating the characteristics of semiconductor devices.
- Simulation reference points on the curve are extracted and used to generate basic training data.
- A deep learning model, specifically a generative adversarial network, is trained based on the basic training data to output a prediction current-voltage curve.
- The deep learning model is then used to generate a target prediction current-voltage curve based on target reference points corresponding to the target semiconductor product.
Potential applications of this technology:
- Semiconductor device characterization and prediction
- Optimization of semiconductor device design and manufacturing processes
- Quality control and yield improvement in semiconductor production
Problems solved by this technology:
- Accurate prediction of semiconductor device characteristics without the need for extensive physical testing
- Efficient analysis and optimization of semiconductor device performance
- Reduction of time and cost in semiconductor device development and production
Benefits of this technology:
- Improved accuracy and efficiency in predicting semiconductor device characteristics
- Faster and more cost-effective development and production of semiconductor devices
- Enhanced quality control and yield improvement in semiconductor manufacturing processes
Original Abstract Submitted
To predict characteristics of a semiconductor device, a simulation current-voltage curve of the semiconductor device is generated using compact models where each compact model generates simulation result data by performing a simulation based on device data. The simulation result data indicate characteristics of semiconductor devices corresponding to the device data. The compact models respectively corresponding to process data and semiconductor products. Simulation reference points on the simulation current-voltage curve are extracted. Basic training data corresponding to a combination of the simulation reference points and the simulation current-voltage curve are generated. A deep learning model is trained based on the basic training data such that the deep learning model outputs a prediction current-voltage curve. A target prediction current-voltage curve is generated based on the deep learning model and target reference points corresponding to the target semiconductor product. The deep learning model is a generative adversarial network.
