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Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices

Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of Fi...

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Detalles Bibliográficos
Autores principales: Chen, An, Ye, Simin, Wang, Zhilong, Han, Yanqiang, Cai, Junfei, Li, Jinjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140614/
https://www.ncbi.nlm.nih.gov/pubmed/37123447
http://dx.doi.org/10.1016/j.patter.2023.100722
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author Chen, An
Ye, Simin
Wang, Zhilong
Han, Yanqiang
Cai, Junfei
Li, Jinjin
author_facet Chen, An
Ye, Simin
Wang, Zhilong
Han, Yanqiang
Cai, Junfei
Li, Jinjin
author_sort Chen, An
collection PubMed
description Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 10(4) cm(2) V(−1) S(−1) and 1.349 × 10(4) cm(2) V(−1) S(−1), respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm.
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spelling pubmed-101406142023-04-29 Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices Chen, An Ye, Simin Wang, Zhilong Han, Yanqiang Cai, Junfei Li, Jinjin Patterns (N Y) Article Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 10(4) cm(2) V(−1) S(−1) and 1.349 × 10(4) cm(2) V(−1) S(−1), respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm. Elsevier 2023-04-06 /pmc/articles/PMC10140614/ /pubmed/37123447 http://dx.doi.org/10.1016/j.patter.2023.100722 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Chen, An
Ye, Simin
Wang, Zhilong
Han, Yanqiang
Cai, Junfei
Li, Jinjin
Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices
title Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices
title_full Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices
title_fullStr Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices
title_full_unstemmed Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices
title_short Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices
title_sort machine-learning-assisted rational design of 2d doped tellurene for fin field-effect transistor devices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140614/
https://www.ncbi.nlm.nih.gov/pubmed/37123447
http://dx.doi.org/10.1016/j.patter.2023.100722
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