Transition metal dichalcogenides (TMDs) with only a few atoms thickness provide an excellent solution to scale down current semiconductor devices. Many studies have demonstrated that molybdenum disulfide (MoS2), a member of TMDs, is promising as a channel material to fabricate field-effect transistors (FETs). However, the carrier mobility in MoS2 FET is always far lower than the theoretical prediction. Although this poor performance can be attributed to the defects, it still lacks a quantitative analysis clarifying the correlation between carrier mobility and defect density. In this work, by using scanning tunneling microscopy, we directly counted the defects in MoS2 FETs with different carrier mobility. We found that vacancies and impurities equally contribute to carrier mobility and the total defect density induces a power-law decreasing tendency to the carrier mobility of MoS2 FET. Our current results directly prove that the reduction of point defects can exponentially improve the carrier mobility of FETs made by TMDs.

Defects to popular two-dimensional (2D) transition metal dichalcogenides (TMDs) seriously lower the efficiency of field-effect transistor (FET) and depress the development of 2D materials. These atomic defects are mainly identified and researched by scanning tunneling microscope (STM) because it can provide precise measurement without harming the samples. The long analysis time of STM for locating defects in images has been solved by combining feature detection with convolutional neural networks (CNN). However, the low signal-noise ratio, insufficient data, and a large amount of TMDs members make the automatic defect detection system hard to be applied. In this study, we propose a deep learning-based atomic defect detection framework (DL-ADD) to efficiently detect atomic defects in molybdenum disulfide (MoS2) and generalize the model for defect detection in other TMD materials. We design DL-ADD with data augmentation, color preprocessing, noise filtering, and a detection model to improve detection quality. The DL-ADD provides precise detection in MoS2 (F2-scores is 0.86 on average) and good generality to WS2 (F2-scores is 0.89 on average).

Plumbene, with a structure similar to graphene, is expected to possess a strong spin-orbit coupling and thus enhances its superconducting critical temperature (T c ). In this work, a buckled plumbene-Au Kagome superstructure grown by depositing Au on Pb(111) is investigated. The superconducting gap monitored by temperature-dependent scanning tunneling microscopy/spectroscopy shows that the buckled plumbene-Au Kagome superstructure not only has an enhanced T c with respect to that of a monolayer Pb but also possesses a higher value than what owned by a bulk Pb substrate. By combining angle-resolved photoemission spectroscopy with density functional theory, the monolayer Au-intercalated low-buckled plumbene sandwiched between the top Au Kagome layer and the bottom Pb(111) substrate is confirmed and the electron-phonon coupling-enhanced superconductivity is revealed. This work demonstrates that a buckled plumbene-Au Kagome superstructure can enhance superconducting T c and Rashba effect, effectively triggering the novel properties of a plumbene.

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