Enhancing Radio Frequency Performance of Graphene Field-Effect Transistors through Machine-Learning-Based Physical Prediction and Optimization-Reference-Cited by-同舟云学术

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Enhancing Radio Frequency Performance of Graphene Field-Effect Transistors through Machine-Learning-Based Physical Prediction and Optimization

Published:2024-05-30 Issue:6 Volume:6 Page:4138-4148
ISSN:2637-6113
Container-title:ACS Applied Electronic Materials
language:en
Short-container-title:ACS Appl. Electron. Mater.

Author:

Seo Gyeong Min¹,Baek Chang-Ki¹²^ORCID,Kong Byoung Don¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea

2. Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea

Funder

Samsung

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsaelm.4c00236

Reference56 articles.

1. A first principles theoretical examination of graphene-based field effect transistors

2. Energy Band-Gap Engineering of Graphene Nanoribbons

3. Graphene nano-ribbon electronics

4. Impact of Size Effect on Graphene Nanoribbon Transport

5. Uniaxial Strain on Graphene: Raman Spectroscopy Study and Band-Gap Opening

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