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Statistical Device Modeling with Arbitrary Model-Parameter Distribution via Markov Chain Monte Carlo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a novel statistical device modeling methodology that can represent model-parameters of arbitrary distribution and correlation. The proposed modeling is based on Markov chain Monte Carlo method in which random samples are drawn from the target probability distribution. The proposed method is also independent of the device models, allowing us to apply the method for any device models. Through the validation, the proposed method successfully reproduced the two peaks of the model parameter distribution that generated the current distribution. In addition, the experiments on the measured current variations following a non-Gaussian distribution demonstrate that the proposed method reduced the modeling error significantly as compared to the conventional method that can only use normal distribution.

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.
Pages192-196
Number of pages5
ISBN (Electronic)9781665406857
DOIs
StatePublished - 27 Sep 2021
Externally publishedYes
Event26th International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2021 - Dallas, United States
Duration: 27 Sep 202129 Sep 2021

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
Period27/09/2129/09/21

Keywords

  • device process variation
  • power MOSFET
  • statistical device modeling

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