Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation

Abstract Two-dimensional (2D) layered materials such as graphene, molybdenum disulfide (MoS2), tungsten disulfide (WSe2), and black phosphorus (BP) provide unique opportunities to identify the origin of current fluctuation, mainly arising from their large surface areas compared with those of their b...

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Autores principales:	Kookjin Lee, Sangjin Nam, Hyunjin Ji, Junhee Choi, Jun-Eon Jin, Yeonsu Kim, Junhong Na, Min-Yeul Ryu, Young-Hoon Cho, Hyebin Lee, Jaewoo Lee, Min-Kyu Joo, Gyu-Tae Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3e001124cfbe40af8b42a1050b945580
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spelling	oai:doaj.org-article:3e001124cfbe40af8b42a1050b9455802021-12-02T18:11:52ZMultiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation10.1038/s41699-020-00186-w2397-7132https://doaj.org/article/3e001124cfbe40af8b42a1050b9455802021-01-01T00:00:00Zhttps://doi.org/10.1038/s41699-020-00186-whttps://doaj.org/toc/2397-7132Abstract Two-dimensional (2D) layered materials such as graphene, molybdenum disulfide (MoS2), tungsten disulfide (WSe2), and black phosphorus (BP) provide unique opportunities to identify the origin of current fluctuation, mainly arising from their large surface areas compared with those of their bulk counterparts. Among numerous material characterization techniques, nondestructive low-frequency (LF) noise measurement has received significant attention as an ideal tool to identify a dominant scattering origin such as imperfect crystallinity, phonon vibration, interlayer resistance, the Schottky barrier inhomogeneity, and traps and/or defects inside the materials and dielectrics. Despite the benefits of LF noise analysis, however, the large amount of time-resolved current data and the subsequent data fitting process required generally cause difficulty in interpreting LF noise data, thereby limiting its availability and feasibility, particularly for 2D layered van der Waals hetero-structures. Here, we present several model algorithms, which enables the classification of important device information such as the type of channel materials, gate dielectrics, contact metals, and the presence of chemical and electron beam doping using more than 100 LF noise data sets under 32 conditions. Furthermore, we provide insights about the device performance by quantifying the interface trap density and Coulomb scattering parameters. Consequently, the pre-processed 2D array of Mel-frequency cepstral coefficients, converted from the LF noise data of devices undergoing the test, leads to superior efficiency and accuracy compared with that of previous approaches.Kookjin LeeSangjin NamHyunjin JiJunhee ChoiJun-Eon JinYeonsu KimJunhong NaMin-Yeul RyuYoung-Hoon ChoHyebin LeeJaewoo LeeMin-Kyu JooGyu-Tae KimNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ChemistryQD1-999ENnpj 2D Materials and Applications, Vol 5, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999 Kookjin Lee Sangjin Nam Hyunjin Ji Junhee Choi Jun-Eon Jin Yeonsu Kim Junhong Na Min-Yeul Ryu Young-Hoon Cho Hyebin Lee Jaewoo Lee Min-Kyu Joo Gyu-Tae Kim Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
description	Abstract Two-dimensional (2D) layered materials such as graphene, molybdenum disulfide (MoS2), tungsten disulfide (WSe2), and black phosphorus (BP) provide unique opportunities to identify the origin of current fluctuation, mainly arising from their large surface areas compared with those of their bulk counterparts. Among numerous material characterization techniques, nondestructive low-frequency (LF) noise measurement has received significant attention as an ideal tool to identify a dominant scattering origin such as imperfect crystallinity, phonon vibration, interlayer resistance, the Schottky barrier inhomogeneity, and traps and/or defects inside the materials and dielectrics. Despite the benefits of LF noise analysis, however, the large amount of time-resolved current data and the subsequent data fitting process required generally cause difficulty in interpreting LF noise data, thereby limiting its availability and feasibility, particularly for 2D layered van der Waals hetero-structures. Here, we present several model algorithms, which enables the classification of important device information such as the type of channel materials, gate dielectrics, contact metals, and the presence of chemical and electron beam doping using more than 100 LF noise data sets under 32 conditions. Furthermore, we provide insights about the device performance by quantifying the interface trap density and Coulomb scattering parameters. Consequently, the pre-processed 2D array of Mel-frequency cepstral coefficients, converted from the LF noise data of devices undergoing the test, leads to superior efficiency and accuracy compared with that of previous approaches.
format	article
author	Kookjin Lee Sangjin Nam Hyunjin Ji Junhee Choi Jun-Eon Jin Yeonsu Kim Junhong Na Min-Yeul Ryu Young-Hoon Cho Hyebin Lee Jaewoo Lee Min-Kyu Joo Gyu-Tae Kim
author_facet	Kookjin Lee Sangjin Nam Hyunjin Ji Junhee Choi Jun-Eon Jin Yeonsu Kim Junhong Na Min-Yeul Ryu Young-Hoon Cho Hyebin Lee Jaewoo Lee Min-Kyu Joo Gyu-Tae Kim
author_sort	Kookjin Lee
title	Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
title_short	Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
title_full	Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
title_fullStr	Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
title_full_unstemmed	Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
title_sort	multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3e001124cfbe40af8b42a1050b945580
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Multiple machine learning approach to characterize two-dimensional nanoelectronic devices via featurization of charge fluctuation

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