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.
Highlights
Low-frequency (LF) 1/f noise spectroscopy is a nondestructive defect diagnosis tool, which identifies dominant scattering origins
This transformation of specific frames into spectral density (SI) allowed the evaluation of periodic spectra, and the amount of spectral energy between frequencies could be obtained by combining the respective used generally for machine learning (ML) and deep learning (DL) in data science; the Fourier frames
It was observed that the Mel scale filter interval was transform (FT) of this data are frequently employed in ML algorithms to improve data interpretation[30,31]
Summary
Low-frequency (LF) 1/f noise spectroscopy is a nondestructive defect diagnosis tool, which identifies dominant scattering origins. Large amount of LF noise data based on the advantages of having a strong statistical foundation and enabling efficient learning from raw sequence data This approach allows us to automatically identify essential device information such as the type of 2D channel materials and gate dielectrics, interface trap density (Nit), Coulomb scattering parameter (αSC), and the presence of chemical and electron beam doping. The combination of factors such as channel material, gate dielectric, contact metal, and electron beam irradiation significantly affects carrier fluctuations as a function of time This combination, which has more than 100 LF noise data sets under 32 conditions, becomes a catalyst for machine learning that automatically and effectively classify the characteristics of various nanoelectronic devices.
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