Electrostatic potential profile generator for two-dimensional semiconductor devices

Seung Cheol Han, Jonghyun Choi, Sung Min Hong

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

3 Citations (Scopus)

Abstract

As efficiency is one of the bottlenecks of device simulation, we propose to employ deep neural networks to generate two-dimensional electrostatic potential profiles for efficiency. Supervising with previously obtained simulation results for various BJT devices, we train deep neural networks to generate an electrostatic potential profile as an initial guess for a non-equilibrium condition with estimating carrier densities by the frozen field simulation. With the generated potential profiles, we significantly reduce the number of Newton iterations without loss of accuracy.

Original languageEnglish
Title of host publication2020 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-300
Number of pages4
ISBN (Electronic)9784863487635
DOIs
Publication statusPublished - 2020 Sep 23
Event2020 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2020 - Virtual, Kobe, Japan
Duration: 2020 Sep 32020 Oct 6

Publication series

NameInternational Conference on Simulation of Semiconductor Processes and Devices, SISPAD
Volume2020-September

Conference

Conference2020 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2020
Country/TerritoryJapan
CityVirtual, Kobe
Period20/9/320/10/6

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). 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).

Publisher Copyright:
© 2020 The Japan Society of Applied Physics.

All Science Journal Classification (ASJC) codes

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

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