Diode‐Like Selective Enhancement of Carrier Transport through Metal–Semiconductor Interface Decorated by Monolayer Boron Nitride

Jaiswal, Hemendra Nath; Liu, Maomao; Shahi, Sharifeh; Wei, Sichen; Lee, Jihea; Chakravarty, Anindita; Guo, Yutong; Wang, Rui-Qiang; Lee, Jung Mu; Chang, Carlin; Fu, Yu; Dixit, R.; Liu, Xiaochi; Yang, Cheng; Yao, Fei; Li, Huamin

doi:10.1002/adma.202002716

Cited by 20 publications

(14 citation statements)

References 62 publications

(92 reference statements)

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“…Conversely, molybdenum disulfide (MoS 2 ), which originates from the transition metal dichalcogenide (TMDC) family, is a semiconductor with an indirect bandgap of~1.89 eV [9,10]. In addition, hexagonal boron nitride (h-BN) is an insulator with a wide gap of~5.9 eV [11,12]. Therefore, considering the diverse properties of different 2D materials, the priority is to study the electrical properties of 2D materials used in manufacturing semiconductor devices.…”

Section: Introductionmentioning

confidence: 99%

Bandgap prediction of two-dimensional materials using machine learning

Liu

Zhang

et al. 2021

PLoS ONE

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The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

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Section: Introductionmentioning

confidence: 99%

Bandgap prediction of two-dimensional materials using machine learning

Liu

Zhang

et al. 2021

PLoS ONE

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“…The formation of a large Schottky barrier and Fermi level pinning have been attributed to various metal-TMD interactions. [14,15] Therefore, an effective technique for reducing the contact resistance requires the formation of metal-insulator-semiconductor (MIS) contacts through the inclusion of an ultrathin tunneling insulator layer [16][17][18][19][20][21] at the metal-TMD interfaces. For instance, the performance of MoS 2 transistors has been enhanced with the incorporation of Transition metal dichalcogenides (TMDs) are of great interest owing to their unique properties.…”

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confidence: 99%

Enhanced Performance of WS₂ Field‐Effect Transistor through Mono and Bilayer h‐BN Tunneling Contacts

Phan

Noh

Kim

et al. 2022

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Transition metal dichalcogenides (TMDs) are of great interest owing to their unique properties. However, TMD materials face two major challenges that limit their practical applications: contact resistance and surface contamination. Herein, a strategy to overcome these problems by inserting a monolayer of hexagonal boron nitride (h‐BN) at the chromium (Cr) and tungsten disulfide (WS2) interface is introduced. Electrical behaviors of direct metal–semiconductor (MS) and metal–insulator–semiconductor (MIS) contacts with mono‐ and bilayer h‐BN in a four‐layer WS2 field‐effect transistor (FET) are evaluated under vacuum from 77 to 300 K. The performance of the MIS contacts differs based on the metal work function when using Cr and indium (In). The contact resistance is significantly reduced by approximately ten times with MIS contacts compared with that for MS contacts. An electron mobility up to ≈115 cm2 V‐1 s‐1 at 300 K is achieved with the insertion of monolayer h‐BN, which is approximately ten times higher than that with MS contacts. The mobility and contact resistance enhancement are attributed to Schottky barrier reduction when h‐BN is introduced between Cr and WS2. The dependence of the tunneling mechanisms on the h‐BN thickness is investigated by extracting the tunneling barrier parameters.

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“…Another route is the insertion of an interface layer or the realization of vdW contact. Although the introduction of a layer of the dielectric at the interface can reduce pinning, it inevitably introduces excess tunnelling barriers 62,63 . The construction of a vdW contact by metal transfer achieves near-ideal Schottky barrier regulation, but may not be suitable for scale integration 64 .…”

Section: Ultrathin Nanosheets For High-performance Transistorsmentioning

confidence: 99%

2D semiconductors for specific electronic applications: from device to system

2022

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The shrinking of transistors has hit a wall of material degradation and the specialized electronic applications for complex scenarios have raised challenges in heterostructures integration. Intriguingly, two-dimensional (2D) materials have excellent performance even at monolayer. The rich band structures and the lattice-mismatch-free heterostructures can further develop specific mechanisms to meet the demands of various electronic systems. Here we review the progress of 2D semiconductors to develop specific electronic applications from devices to systems. Focusing on the ultra-thin high-performance nanosheets for transistor channels, we consider channel optimization, contact characteristics, dielectric integration. Then we examined 2D semiconductors for specific electronic functions including computing, memory and sense. Finally, we discuss the specific applications of functionalized arrays aiming at problems that are difficult to solve with bulk materials, like the fusion of memory and computation and the all-in-one system.

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Diode‐Like Selective Enhancement of Carrier Transport through Metal–Semiconductor Interface Decorated by Monolayer Boron Nitride

Cited by 20 publications

References 62 publications

Bandgap prediction of two-dimensional materials using machine learning

Bandgap prediction of two-dimensional materials using machine learning

Enhanced Performance of WS₂ Field‐Effect Transistor through Mono and Bilayer h‐BN Tunneling Contacts

2D semiconductors for specific electronic applications: from device to system

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Diode‐Like Selective Enhancement of Carrier Transport through Metal–Semiconductor Interface Decorated by Monolayer Boron Nitride

Cited by 20 publications

References 62 publications

Bandgap prediction of two-dimensional materials using machine learning

Bandgap prediction of two-dimensional materials using machine learning

Enhanced Performance of WS2 Field‐Effect Transistor through Mono and Bilayer h‐BN Tunneling Contacts

2D semiconductors for specific electronic applications: from device to system

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Product

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Enhanced Performance of WS₂ Field‐Effect Transistor through Mono and Bilayer h‐BN Tunneling Contacts