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This letter reports a highly scaled 90 nm gate length beta-Ga2O3 T-gate MOSFET with no current collapse and record power gain cut off frequency (fMAX). The epitaxial stack of 60 nm thin channel MOSFET was grown by Molecular Beam Epitaxy (MBE) and highly doped (n++) contact regrowth was carried out by Metal Organic Chemical Vapour Deposition (MOCVD) in the source/drain region. Maximum on current (IDS, MAX) of 160 mA/mm and transconductance (gm) around 36 mS/mm was measured at VDS= 10 V for LSD= 1.5 micrometer channel length. Transconductance is limited by higher channel sheet resistance (Rsheet). We observed no current collapse for both drain and gate lag measurement even at higher VDG,Q quiescent bias points. This is the first report of Ga2O3 FET showing no current collapse without any external passivation. Breakdown voltage around 125 V was reported for LGD= 1.2 micrometer. We extracted 27 GHz current gain cut off frequency (fT) and 55 GHz fMAX for 20 V drain bias. fMAX value mentioned here is the highest for Ga2O3 and the first demonstration of 55 GHz operation. fT. VBR product of 3.375 THz.V has been calculated which is comparable with state-of-art GaN HEMT. This letter suggests that Ga2O3 can be a suitable candidate for X-band application.

The lack of p-type doping has impeded the development of vertical gallium oxide (Ga2O3) devices. Current blocking layers (CBL) using implanted deep acceptors has been used to demonstrate vertical devices. This paper presents the first demonstration of in situ Mg-doped beta-Ga2O3 CBLs grown using metalorganic chemical vapor deposition. Device structures were designed with in-situ Mg doped layers with varied targeted Mg doping concentrations, which were calibrated by quantitative secondary ion mass spectroscopy (SIMS). The effectiveness of the CBL is characterized using temperature dependent current-voltage measurements using n-Mg-doped-n structures, providing crucial insight into the underlying mechanisms. To further validate the experimental results, a TCAD simulation is performed and the electrically active effective doping is found to be dependent on the Mg-doping density, offering a new perspective on the optimization of CBL performance. Breakdown measurements show a 3.4 MV/cm field strength. This study represents a significant step forward in the development of Ga2O3-based devices and paves the way for future advancements in this exciting field.

Phase pure \b{eta}-(AlxGa1-x)2O3 thin films are grown on (001) oriented \b{eta}-Ga2O3 substrates via metalorganic chemical vapor deposition (MOCVD). By systematically tuning the precursor molar flow rates, the epitaxial growth of coherently strained \b{eta}-(AlxGa1-x)2O3 films are demonstrated with up to 25% Al compositions as evaluated by high resolution x-ray diffraction (XRD). The asymmetrical reciprocal space mapping confirms the growth of coherent \b{eta}-(AlxGa1-x)2O3 films (x < 25%) on (001) \b{eta}-Ga2O3 substrates. While the films show smooth surface morphologies, the alloy inhomogeneity with local segregation of Al along (-201) plane is observed from atomic resolution STEM imaging, resulting in wavy and inhomogeneous interfaces in \b{eta}-(AlxGa1-x)2O3/\b{eta}-Ga2O3 superlattice structure. Room temperature Raman spectra of \b{eta}-(AlxGa1-x)2O3 films show similar characteristics peaks as (001) \b{eta}-Ga2O3 substrate without obvious Raman shifts for films with different Al compositions. Atom probe tomography (APT) was used to investigate the atomic level structural chemistry with increasing Al content in the \b{eta}-(AlxGa1-x)2O3 films. A monotonous increase in chemical heterogeneity is observed from the in-plane Al/Ga distributions which was further confirmed via statistical frequency distribution analysis (FDA). Although the films exhibit alloy fluctuations, n-type doping with good electrical properties are demonstrated for films with various Al compositions. The determined valence and conduction band offsets at \b{eta}-(AlxGa1-x)2O3/\b{eta}-Ga2O3 heterojunctions using x-ray photoelectron spectroscopy (XPS) reveal the formation of type-II (staggered) band alignment.

This letter reports high-performance $\mathrm{\beta} Ga2O3 thin channel MOSFETs with T-gate and degenerately doped source/drain contacts regrown by MOCVD. Gate length scaling (LG= 160-200 nm) leads to a peak drain current (ID,MAX) of 285 mA/mm and peak trans-conductance (gm) of 52 mS/mm at 10 V drain bias with 23.5 Ohm mm on resistance (Ron). A low metal/n+ contact resistance of 0.078 Ohm mm was extracted from TLM measurement. Ron is dominated by interface resistance between channel and regrown layer. A gate-to-drain breakdown voltage of 192 V is measured for LGD = 355 nm resulting in average breakdown field (E_AVG) of 5.4 MV/cm. This E_AVG is the highest reported among all sub-micron gate length lateral FETs. RF measurements on 200 nm Silicon Nitride (Si3N4) passivated device shows a current gain cut off frequency (f_T) of 11 GHz and record power gain cut off frequency (f_MAX) of 48 GHz. The f_T.V_Br product is 2.11 THz.V for 192 V breakdown voltage. The switching figure of merit exceeds that of silicon and is comparable to mature wide-band gap devices.

Experiments have found that diffusion in metabolically active cells is much faster than in dormant cells, especially for large particles. However, the mechanism of this size-dependent diffusion enhancement in living cells is still unclear. In this work, we approximate the net effect of metabolic processes as a white-noise active force and simulate a model system of bacterial cytoplasm with a highly polydisperse particle size distribution. We find that diffusion enhancement in active cells relative to dormant cells can be more substantial for large particles. Our simulations agree quantitatively with the experimental data of Escherichia coli, suggesting an autocorrelation function of the active force proportional to the cube of particle radius. We demonstrate that such a white-noise active force is equivalent to an active force of about 0.57 pN with random orientation. Our work unveils an emergent simplicity of random processes inside living cells., Comment: 20 pages, 18 figures

The in-situ metalorganic chemical vapor deposition (MOCVD) growth of Al$_2$O$_3$ dielectrics on $\beta$-Ga$_2$O$_3$ and $\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ films is investigated as a function of crystal orientations and Al compositions of $\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ films. The interface and film qualities of Al$_2$O$_3$ dielectrics are evaluated by high resolution X-ray diffraction (HR-XRD) and scanning transmission electron microscopy (HR-STEM) imaging, which indicate the growth of high quality amorphous Al$_2$O$_3$ dielectrics with abrupt interfaces on (010), (100) and (-201) oriented $\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ films. The surface stoichiometries of Al$_2$O$_3$ deposited on all orientations of $\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ are found to be well maintained with a bandgap energy of 6.91 eV as evaluated by high resolution x-ray photoelectron spectroscopy, which is consistent with the atomic layer deposited (ALD) Al$_2$O$_3$ dielectrics. The evolution of band offsets at both in-situ MOCVD and ex-situ ALD deposited Al$_2$O$_3$/$\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ are determined as a function of Al composition, indicating the influence of the deposition method, orientation, and Al composition of $\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ films on resulting band alignments. Type II band alignments are determined at the MOCVD grown Al$_2$O$_3$/$\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ interfaces for (010) and (100) orientations, whereas type I band alignments with relatively lower conduction band offsets are observed along (-201) orientation. Results from this work revealed that the in-situ MOCVD deposited high quality Al$_2$O$_3$ dielectrics with sharp interfaces can be considered as a viable alternative of commonly used ex-situ deposited (ALD) Al$_2$O$_3$ for developing high performance $\beta$-Ga$_2$O$_3$ and $\beta$-(Al$_x$Ga$_{1-x}$)$_2$O$_3$ based devices.

Epitaxial growth of \k{appa}-phase Ga2O3 thin films are investigated on c-plane sapphire, GaN- and AlNon-sapphire, and (100) oriented yttria stabilized zirconia (YSZ) substrates via metalorganic chemical vapor deposition (MOCVD). The structural and surface morphological properties are investigated by comprehensive material characterization. Phase pure \k{appa}-Ga2O3 films are successfully grown on GaN-, AlN-on sapphire, and YSZ substrates through a systematical tuning of the growth parameters including the precursor molar flow rates, chamber pressure and growth temperature, whereas the growth on c-sapphire substrates leads to a mixture of \b{eta}- and \k{appa}polymorphs of Ga2O3 under the investigated growth conditions. The influence of the crystalline structure, surface morphology and roughness of \k{appa}-Ga2O3 films grown on different substrates are investigated as a function of precursor flow rate. High resolution scanning transmission electron microscopy (HR-STEM) imaging of \k{appa}-Ga2O3 films reveals abrupt interfaces between the epitaxial film and the sapphire, GaN and YSZ substrates. The growth of single crystal orthorhombic \k{appa}Ga2O3 films is confirmed by analyzing the STEM nano-diffraction pattern. The chemical composition, surface stoichiometry, and the bandgap energies of \k{appa}-Ga2O3 thin films grown on different substrates are studied by high resolution x-ray photoelectron spectroscopy (XPS) measurements. The type-II (staggered) band alignments at three interfaces between \k{appa}-Ga2O3 and c-sapphire, AlN, and YSZ substrates are determined by XPS, with the exception of \k{appa}-Ga2O3/GaN interface, which shows type I (straddling) band alignment.

Timely evacuation is crucial to disaster response, as people can avoid suffering and loss of lives when a major disaster happens. With the development of sharing economy, ridesharing has the advantage of reducing congestion, saving travel time, and optimizing transportation mode to improve disaster evacuation efficiency. The paper proposes to integrate the concept of ridesharing into evacuation and develops a mixed-integer programming model for this problem. A real-world case study based on Houston is used to validate the proposed model. A series of instances are designed to compare the evacuation efficiency using two indicators, evacuation percentage and average travel distance. Results reveal that increasing the number of vehicles to help carless individuals might not be the most efficient method in this model. Moreover, this model offers a specific response strategy based on different disaster scales, which not only develops a better evacuation plan for the people but also provides relief agencies insights on resource utilization., Comment: 6 pages, 3 figures. Proceeding of the 2020 IISE Annual Conference, https://www.proquest.com/openview/403992

In this paper, we have demonstrated the large-size free-standing single-crystal b-Ga2O3 NMs fabricated by the hydrogen implantation and lift-off process directly from MOCVD grown b-Ga2O3 epifilms on native substrates. The optimum implantation conditions were simulated with a Monte-Carlo simulation to obtain the high hydrogen concentration with a narrow ion distribution at the desired depth. Two as grown b-Ga2O3 samples with different orientation ([100] and [001]) were used and successfully create 1.2 um thick b-Ga2O3 NMs without any physical damages. These b-Ga2O3 NMs were then transfer-printed onto rigid and flexible substrates such as SiC substrate and polyimide substrate. Various material characterizations were performed to investigate the crystal quality, surface morphology, optical property, mechanical property, and bandgap before and after the lift-off and revealed that good material quality is maintained. This result offers several benefits in that the thickness, doping, and size of b-Ga2O3 NMs can be fully controlled. Moreover, more advanced b-Ga2O3-based NM structures such as (AlxGa1-x)2O3/Ga2O3 heterostructure NMs can be directly created from their bulk epitaxy substrates thus this result provides a viable route for the realization of high performance b-Ga2O3 NM-based electronics and optoelectronics that can be built on various substrates and platforms.

Social media has become an essential channel for posting disaster-related information, which provide governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes towards disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of people's behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management., Comment: 24 pages, 14 figures

With the development of the Internet, social media has become an important channel for posting disaster-related information. Analyzing attitudes hidden in these texts, known as sentiment analysis, is crucial for the government or relief agencies to improve disaster response efficiency, but it has not received sufficient attention. This paper aims to fill this gap by focusing on investigating attitudes towards disaster response and analyzing targeted relief supplies during disaster response. The contributions of this paper are fourfold. First, we propose several machine learning models for classifying public sentiment concerning disaster-related social media data. Second, we create a natural disaster dataset with sentiment labels, which contains nearly 50,00 Twitter data about different natural disasters in the United States (e.g., a tornado in 2011, a hurricane named Sandy in 2012, a series of floods in 2013, a hurricane named Matthew in 2016, a blizzard in 2016, a hurricane named Harvey in 2017, a hurricane named Michael in 2018, a series of wildfires in 2018, and a hurricane named Dorian in 2019). We are making our dataset available to the research community: https://github.com/Dong-UTIL/Natural-Hazards-Twitter-Dataset. It is our hope that our contribution will enable the study of sentiment analysis in disaster response. Third, we focus on extracting public attitudes and analyzing the essential needs (e.g., food, housing, transportation, and medical supplies) for the public during disaster response, instead of merely targeting on studying positive or negative attitudes of the public to natural disasters. Fourth, we conduct this research from two different dimensions for a comprehensive understanding of public opinion on disaster response, since disparate hazards caused by different types of natural disasters., Comment: 3 pages. The dataset is available on GitHub at this address: : https://github.com/Dong-UTIL/Natural-Hazards-Twitter-Dataset