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Register a new userDesign and Demonstration of Ultra Wide Bandgap AlGaN Tunnel Junctions
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Ultra violet light emitting diodes (UV LEDs) face critical limitations in both the injection efficiency and light extraction efficiency due to the resistive and absorbing p-type contact layers. In this work, we investigate the design and application of polarization engineered tunnel junctions for ultra-wide bandgap AlGaN (Al mole fraction higher than 50%) materials towards highly efficient UV LEDs. We demonstrate that polarization-induced 3D charge is beneficial in reducing tunneling barriers especially for high composition AlGaN tunnel junctions. The design of graded tunnel junction structures could lead to low tunneling resistance below 10-3 Ohm cm2 and low voltage consumption below 1 V (at 1 kA/cm2) for high composition AlGaN tunnel junctions. Experimental demonstration of 292 nm emission was achieved through non-equilibrium hole injection into wide bandgap materials with bandgap energy larger than 4.7 eV, and detailed modeling of tunnel junctions shows that they can be engineered to have low resistance, and can enable efficient emitters in the UV-C wavelength range.
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The large electronic polarization in III-V nitrides allow for novel physics not possible in other semiconductor families. In this work, interband Zener tunneling in wide-bandgap GaN heterojunctions is demonstrated by using polarization-induced electric fields. The resulting tunnel diodes are more conductive under reverse bias, which has applications for zero-bias rectification and mm-wave imaging. Since interband tunneling is traditionally prohibitive in wide-bandgap semiconductors, these polarization-induced structures and their variants can enable a number of devices such as multijunction solar cells that can operate under elevated temperatures and high fields.
Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of the vast chemical design space. Herein we present an active generative inverse design method that combines active learning with a deep variational autoencoder neural network and a generative adversarial deep neural network model to discover new materials with a target property in the whole chemical design space. The application of this method has allowed us to discover new thermodynamically stable materials with high band gap (SrYF$_5$) and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$, YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the first principle DFT calculations. Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model. The experiments show the effectiveness of our active generative inverse design approach.
We carefully investigate three important effects including postgrowth activation annealing, delta ({delta}) dose and p+-GaN layer thickness and experimentally demonstrate their influence on the electrical properties of GaN p-n homojunction diodes with a tunnel junction (TJ)-based p-contact. The p-n diodes and TJ structures were monolithically grown by metalorganic chemical vapor deposition (MOCVD) in a single growth step. By optimizing the annealing time and temperature for magnesium (Mg) activation and introducing {delta}-doses for both donors and acceptors at TJ interfaces, a significant improvement in electrical properties is achieved. For the continuously-grown, all-MOCVD GaN homojunction TJs, ultra-low forward voltage penalties of 158 mV and 490 mV are obtained at current densities of 20 A/cm2 and 100 A/cm2, respectively. The p-n diode with an engineered TJ shows a record-low normalized differential resistance of 1.6 x 10-4 {Omega}-cm2 at 5 kA/cm2.
A pressure-induced phase transition, associated with an increase of the coordination number of In and Ta, is detected beyond 13 GPa in InTaO4 by combining synchrotron x-ray diffraction and Raman measurements in a diamond anvil cell with ab-initio calculations. High-pressure optical-absorption measurements were also carried out. The high-pressure phase has a monoclinic structure which shares the same space group with the low-pressure phase (P2/c). The structure of the high-pressure phase can be considered as a slight distortion of an orthorhombic structure described by space group Pcna. The phase transition occurs together with a unit-cell volume collapse and an electronic bandgap collapse observed by experiments and calculations. Additionally, a band crossing is found to occur in the low-pressure phase near 7 GPa. The pressure dependence of all the Raman-active modes is reported for both phases as well as the pressure dependence of unit-cell parameters and the equations of state. Calculations also provide information on IR-active phonons and bond distances. These findings provide insights into the effects of pressure on the physical properties of InTaO4.
Magnetic tunnel junctions operating in the superparamagnetic regime are promising devices in the field of probabilistic computing, which is suitable for applications like high-dimensional optimization or sampling problems. Further, random number generation is of interest in the field of cryptography. For such applications, a devices uncorrelated fluctuation time-scale can determine the effective system speed. It has been theoretically proposed that a magnetic tunnel junction designed to have only easy-plane anisotropy provides fluctuation rates determined by its easy-plane anisotropy field, and can perform on nanosecond or faster time-scale as measured by its magnetoresistances autocorrelation in time. Here we provide experimental evidence of nanosecond scale fluctuations in a circular shaped easy-plane magnetic tunnel junction, consistent with finite-temperature coupled macrospin simulation results and prior theoretical expectations. We further assess the degree of stochasticity of such signal.
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