Device simulations with A U-Net model predicting physical quantities in two-dimensional landscapes

Although Technology Computer-Aided Design (TCAD) simulation has paved a successful and efficient way to significantly reduce the cost of experiments under the device design, it still encounters many challenges as the semiconductor industry goes through rapid development in recent years, i.e. Complex...

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Bibliographic Details
Published in:Scientific reports 2023-01, Vol.13 (1), p.731-731, Article 731
Main Authors: Lee, Wen-Jay, Hsieh, Wu-Tsung, Fang, Bin-Horn, Kao, Kuo-Hsing, Chen, Nan-Yow
Format: Article
Language:English
Subjects:
639/166/987
639/705/1042
Computer Simulation
Deep learning
Design
Electric fields
Humanities and Social Sciences
Machine Learning
multidisciplinary
Neural networks
Neural Networks, Computer
Optimization
Physical Examination
Physics
Potential energy
Predictions
Science
Science (multidisciplinary)
Semiconductors
Simulation
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Summary:Although Technology Computer-Aided Design (TCAD) simulation has paved a successful and efficient way to significantly reduce the cost of experiments under the device design, it still encounters many challenges as the semiconductor industry goes through rapid development in recent years, i.e. Complex 3D device structures, power devices. Recently, although machine learning has been proposed to enable the simulation acceleration and inverse‑design of devices, which can quickly and accurately predict device performance, up to now physical quantities (such as electric field, potential energy, quantum-mechanically confined carrier distributions, and so on) being essential for understanding device physics can still only be obtained by traditional time-consuming self-consistent calculation. In this work, we employ a modified U-Net and train the models to predict the physical quantities of a MOSFET in two-dimensional landscapes for the first time. Errors in predictions by the two models have been analyzed, which shows the importance of a sufficient amount of data to prediction accuracy. The computation time for one landscape prediction with high accuracy by our well-trained U-Net model is much faster than the traditional approach. This work paves the way for interpretable predictions of device simulations based on convolutional neural networks.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-27599-z