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

Seung Cheol Han; Jonghyun Choi; Sung Min Hong

doi:10.1109/SISPAD54002.2021.9592540

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

Seung Cheol Han, Jonghyun Choi, Sung Min Hong

Department of Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	SISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	52-55
Number of pages	4
ISBN (Electronic)	9781665406857
DOIs	https://doi.org/10.1109/SISPAD54002.2021.9592540
Publication status	Published - 2021 Sep 27
Event	26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021 - Dallas, United States Duration: 2021 Sep 27 → 2021 Sep 29

Publication series

Name	International Conference on Simulation of Semiconductor Processes and Devices, SISPAD
Volume	2021-September

Conference

Conference	26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021
Country/Territory	United States
City	Dallas
Period	21/9/27 → 21/9/29

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

Access to Document

10.1109/SISPAD54002.2021.9592540

Cite this

Han, S. C., Choi, J., & Hong, S. M. (2021). Acceleration of Three-Dimensional Device Simulation with the 3D Convolutional Neural Network. In SISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings (pp. 52-55). (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD; Vol. 2021-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SISPAD54002.2021.9592540

Han, Seung Cheol ; Choi, Jonghyun ; Hong, Sung Min. / Acceleration of Three-Dimensional Device Simulation with the 3D Convolutional Neural Network. SISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 52-55 (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD).

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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.",

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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: {\textcopyright} 2021 IEEE.; 26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021 ; Conference date: 27-09-2021 Through 29-09-2021",

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Han, SC, Choi, J & Hong, SM 2021, Acceleration of Three-Dimensional Device Simulation with the 3D Convolutional Neural Network. in SISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings. International Conference on Simulation of Semiconductor Processes and Devices, SISPAD, vol. 2021-September, Institute of Electrical and Electronics Engineers Inc., pp. 52-55, 26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021, Dallas, United States, 21/9/27. https://doi.org/10.1109/SISPAD54002.2021.9592540

Acceleration of Three-Dimensional Device Simulation with the 3D Convolutional Neural Network. / Han, Seung Cheol; Choi, Jonghyun; Hong, Sung Min.

SISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 52-55 (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD; Vol. 2021-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Han, Seung Cheol

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N1 - 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.

PY - 2021/9/27

Y1 - 2021/9/27

N2 - 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.

AB - 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.

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Han SC, Choi J, Hong SM. Acceleration of Three-Dimensional Device Simulation with the 3D Convolutional Neural Network. In SISPAD 2021 - 2021 International Conference on Simulation of Semiconductor Processes and Devices, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 52-55. (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD). doi: 10.1109/SISPAD54002.2021.9592540

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

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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