Search

Electronic devices would benefit from a low-cost amorphous, dopable, bipolar oxide semiconductor. However, p-type oxides are quite rare, largely due to self-compensation by native defects. Our simulations find that the amorphous phase of TeO2 is chemically ordered, forms shallow, uncompensated acceptor substitutional AsTe and NO centers, and uses materials that are processable at low temperatures. [ABSTRACT FROM AUTHOR]

Solid–solid interfaces play a critical role in determining the performance of materials and devices. However, both experimental and theoretical determinations of interface structures are very challenging. In this Letter, we develop a high-throughput interface prediction and generation (IPG) scheme to predict the atomic structure of interfaces. To demonstrate the performance of the IPG, we apply it to the β-Ga2O3/AlN interface, which has attracted a great deal of attention for its promising application in power devices. Six β-Ga2O3/AlN interfaces are screened from thousands of candidates with the minimum mismatch and area tolerance. All these interfaces are modeled to satisfy the electron counting rules and then optimized by the minimum hopping method. The band alignments at the β-Ga2O3/AlN interfaces are in accordance with the available theoretical and experimental results. The results provide the basis for designing optical and electronic devices based on β-Ga2O3/AlN interfaces. [ABSTRACT FROM AUTHOR]

Understanding the physical mechanism behind atomic-size dependence of the bandgap, phonon frequency, and mechanical strength in various monolayered MA2Z4 is of crucial importance for their electronic and photoelectronic applications. The density functional theory calculation results confirm that these physical quantities gradually decrease with the increasing periodicity of the atomic size (or radius) of the A or Z of MA2Z4. In order to clarify the common origin of the atomic-size dependence of these quantities, we establish these quantities as functions of bond length and bond energy by developing a bond relaxation theory approach. Theoretical reproduction of periodic trends confirms that bond expansion and energy weakening dominate their atomic-size dependence. The proposed approach is not only helpful to understand the physical origins of atomic-size dependence in different MA2Z4 monolayers but also can be extended to study the periodic trends of the related physical properties in other systems. [ABSTRACT FROM AUTHOR]

Diamond has compelling advantages in power devices as an ultrawide-bandgap semiconductor. Using first-principles calculations, we systematically investigate the structural and electronic properties of hydrogen-terminated diamond (H-diamond) (111) van der Waals (vdW) heterostructures with graphite and hexagonal boron nitride (h-BN) layers. The graphite/H-diamond heterostructure forms a p-type ohmic contact and the p-type Schottky barrier decreases as the number of graphite layers increases. In contrast, the h-BN/H-diamond heterostructure exhibits semiconducting properties and a tunable type-II band alignment. Moreover, the charge transfer is concentrated at the interface with a large amount of charge accumulating on the C–H bonds on the H-diamond (111) surface, indicating the formation of a highly conductive two-dimensional hole gas (2DHG) layer. In a similar vein, the promising structural and electronic properties of graphite, h-BN, and H-diamond (111) in the graphite/h-BN/H-diamond (111) vdW heterostructure are well preserved upon their contact, while such heterostructure exhibits flexible band offset and Schottky contacts. These studies of interface engineering for H-diamond heterostructures are expected to advance the application of 2D materials in H-diamond field effect transistors, which is an important development in the design of surface transfer doping for 2DHG H-diamond devices. [ABSTRACT FROM AUTHOR]

This study proposes a three‐phase direct matrix converter (DMC) topology based on an improved quasi‐Z source (QZS) network. The proposed topology shows the following features: (i) Improve the voltage transmission ratio of traditional DMC. (ii) The voltage and current on the output side can be affected by the input side to the minimum, which minimises the harmonic content of the output current. (iii) Due to the existence of the improved QZS network, the three bidirectional switches of each output phase bridge arm are turned‐on at the same time, which improves the reliability of the system. (iv) The improved QZS network has an input LC filter function, and no additional filter circuit is required on the input side. This study analyses the topological structure of the improved QZS‐DMC and elaborates its boost working principle. On this basis, an improved dual space vector modulation strategy with shoot‐through zero vector is proposed, which can effectively reduce the loss of the switch tube and improve the output waveform quality of the converter. Simulation and experimental results verify the proposed topology and strategy. [ABSTRACT FROM AUTHOR]

In this paper, a single-phase extended-boost quasi-Z source (EqZS) cascaded multilevel inverter (CMI) for photovoltaic power systems is proposed. A phase-shifted pulse width amplitude modulation strategy based on dual switching frequency modulation (DFM-PWAM) for the inverter is also presented. Compared to the traditional quasi-Z source inverter, the EqZS network can significantly increase the dc-link voltage and achieve a higher voltage gain. Due to the inherent double-line-frequency (2ω) voltage and current ripples in the single-phase EqZS inverter (EqZSI), an accurate equivalent model based on the EqZSI module is established to design rational parameters of the EqZS network, which can limit the 2ω ripples in the required range. However, large capacitance and inductance are needed to restrain 2ω ripples through this method, which results in a bulky impedance-source network. Higher switching frequency can reduce the size of the impedance-source network but will increase the switching losses due to hard-switching. The proposed DFM-PWAM technique can not only keep a compact impedance-source network but also mitigate the switching frequency effect on the switching losses. Finally, simulation and experimental results validate the feasibility of the proposed topology and modulation technique and the correctness of theory. [ABSTRACT FROM AUTHOR]

Diamond has become a promising candidate for high-power devices based on its ultrawide bandgap and excellent thermoelectric properties, where an appropriate gate dielectric has been a bottleneck hindering the development of diamond devices. Herein, we have systematically investigated the structural arrangement and electronic properties of diamond/high-κ oxide (HfO 2 , ZrO 2 ) heterojunctions by first-principles calculations with a SiO 2 interlayer. Charge analysis reveals that the C-Si bonding interface attracts a large amount of charge concentrated at the diamond interface, indicating the potential for the formation of a 2D hole gas (2DHG). The diamond/HfO 2 and diamond/ZrO 2 heterostructures exhibit similar "Type II" band alignments with VBOs of 2.47 and 2.21 eV, respectively, which is consistent with experimental predictions. The introduction of a SiO 2 dielectric layer into the diamond/SiO 2 /high-κ stacks exhibits the typical "Type I″ straddling band offsets (BOs). In addition, the wide bandgap SiO 2 interlayer keeps the valence band maximum (VBM) and conduction band minimum (CBM) in the stacks away from those of diamond, effectively confining the electrons and holes in MOS devices. This work exhibits the potential of SiO 2 /high-κ oxide gate dielectrics for diamond devices and provides theoretical insights into the rational design of high-quality gate dielectrics for diamond-based MOS device applications.

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration., (© 2023 Wiley-VCH GmbH.)