Acceleration of Three-Dimensional Device Simulation with the 3D Convolutional Neural Network

Seung Cheol Han, Jonghyun Choi, Sung Min Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose to use a 3D convolutional neural network to accelerate three-dimensional device simulation by generating an electrostatic potential profile. In the training phase, the deep neural network is trained with the simulation results for various 3D MOSFETs in a supervised manner. The generated potential profile is used as an initial guess at a non-equilibrium condition, while carrier densities are estimated by the frozen field simulation. By numerical examples for three-dimensional MOSFETs, we show that the proposed method significantly reduces the number of the Newton iterations.

Original languageEnglish
Title of host publicationSISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-55
Number of pages4
ISBN (Electronic)9781665406857
DOIs
Publication statusPublished - 2021 Sep 27
Event26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021 - Dallas, United States
Duration: 2021 Sep 272021 Sep 29

Publication series

NameInternational Conference on Simulation of Semiconductor Processes and Devices, SISPAD
Volume2021-September

Conference

Conference26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021
Country/TerritoryUnited States
CityDallas
Period21/9/2721/9/29

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2019R1A2C1086656 and NRF-2020M3H4A3081800). This work was also supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2019-0-01351, Development of Ultra Low-Power Mobile Deep Learning Semiconductor with Compression/Decompression of Activation/Kernel Data, 30 %)

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Modelling and Simulation

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