RESUMO
Molybdenum disulfide (MoS2) is a promising candidate for next-generation transistor channel materials, boasting outstanding electrical properties and ultrathin structure. Conventional ion implantation processes are unsuitable for atomically thin two-dimensional (2D) materials, necessitating nondestructive doping methods. We proposed a novel approach: tunable n-type doping through sulfur vacancies (VS) and p-type doping by nitrogen substitution in MoS2, controlled by the duration of NH3 plasma treatment. Our results reveal that NH3 plasma exposure of 20 s increases the 2D sheet carrier density (n2D) in MoS2 field-effect transistors (FETs) by +4.92 × 1011 cm-2 at a gate bias of 0 V, attributable to sulfur vacancy generation. Conversely, treatment of 40 s reduces n2D by -3.71 × 1011 cm-2 due to increased nitrogen doping. X-ray photoelectron spectroscopy, Raman spectroscopy, and photoluminescence analyses corroborate these electrical characterization results, indicating successful n- and p-type doping. Temperature-dependent measurements show that the Schottky barrier height at the metal-semiconductor contact decreases by -31 meV under n-type conditions and increases by +37 meV for p-type doping. This study highlights NH3 plasma treatment as a viable doping method for 2D materials in electronic and optoelectronic device engineering.
RESUMO
Molybdenum disulfide (MoS2 ), a metal dichalcogenide, is a promising channel material for highly integrated scalable transistors. However, intrinsic donor defect states, such as sulfur vacancies (Vs ), can degrade the channel properties and lead to undesired n-doping. A method for healing the donor defect states in monolayer MoS2 is proposed using oxygen plasma, with an aluminum oxide (Al2 O3 ) barrier layer that protects the MoS2 channel from damage by plasma treatment. Successful healing of donor defect states in MoS2 by oxygen atoms, even in the presence of an Al2 O3 barrier layer, is confirmed by X-ray photoelectron spectroscopy, photoluminescence, and Raman spectroscopy. Despite the decrease in 2D sheet carrier concentration (Δn2D = -3.82×1012 cm-2 ), the proposed approach increases the on-current and mobility by 18% and 44% under optimal conditions, respectively. Metal-insulator transition occurs at electron concentrations of 5.7×1012 cm-2 and reflects improved channel quality. Finally, the activation energy (Ea ) reduces at all the gate voltages (VG ) owing to a decrease in Vs , which act as a localized state after the oxygen plasma treatment. This study demonstrates the feasibility of plasma-assisted healing of defects in 2D materials and electrical property enhancement and paves the way for the development of next-generation electronic devices.
RESUMO
BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. PURPOSE: The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture. METHODS: The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models. RESULTS: The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. CONCLUSION: Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.
