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The spinel zinc gallate ZnGa2O4 stands out among the emerging ultra-wide bandgap (∼5 eV) semiconductors as the ternary complex oxide with the widest gap where bipolar conductivity has been demonstrated. For power and energy electronics, a fundamental property of the material is its critical electric field ( E CR ) although, for ZnGa2O4, is yet unknown. In this work, highly resistive p-type ZnGa2O4 thin films on sapphire and Si substrates were grown by metal organic chemical vapor deposition to determine both, the remote acceptor concentration and vertical breakdown voltage. Hall Effect measurements confirmed a low carrier concentration at room temperature of ∼1011 cm−3. From vertical metal-semiconductor-metal structures the average E CR has been estimated to be of at least 5.3 MV cm−1, which already is significantly larger than the one of SiC and GaN.

Critical electric fields (E C) of lithium-doped p+-nickel oxide (NiO) were investigated by the capacitance (C)–voltage (V) and current (I)–V measurements using p+-NiO/n+-gallium oxide (Ga2O3) heterojunction diodes. The E C was estimated by device simulations using the net acceptor concentrations (N A) obtained from C–V measurements and breakdown voltages obtained from reverse I–V characteristics. The E C of NiO depended on the N A of the NiO and ranged from 5.4 to 10.1 MV cm−1. Large E C was obtained for high N A. NiO was confirmed to be one of the promising p-type oxides to realize high-power p-n heterojunction devices with Ga2O3 due to the high E C.

Low power-loss semiconductor devices are necessary to achieve a carbon-neutral society. The optimization of device structures is known as a time-consuming process. In this work, we investigated an optimization approach with the help of machine learning. We applied an active learning scheme to optimize a gallium oxide Schottky barrier diode structure and demonstrated how this approach helps to reduce the number of time-consuming simulations for the optimization process. For the investigated work, the active learning strategy almost reduced the number of simulations by a factor of 2 in contrast to the conventional genetic optimization. In addition, we also demonstrated that machine learning models can be used to estimate the performance variations caused by process variations. This approach can also contribute to reducing the number of simulations and speeding up the structure design process.

Ultra-wide bandgap gallium oxide (∼5 eV) has emerged as a novel semiconductor platform for extending the current limits of power electronics and deep ultraviolet optoelectronics at a predicted fraction of cost. Finding effective acceptor dopant for gallium oxide is a hot issue. One element that quite often is considered as a potential candidate is zinc. A number of experimental works have reported the signature of Zn-acceptor, but the direct evidence of hole conductivity was missing. In this work, p-type Zn-doped Ga2O3 thin films were grown by the metal-organic chemical vapour deposition technique on sapphire substrates. By high-temperature Hall effect measurements, Zn related acceptor level ionization energy as 0.77 eV above the valence band maximum was determined. Additionally, we have carried out the simulation study regarding the application of the Zn:Ga2O3 semi-insulating material, to be used as a guard ring for improving the high voltage performance of the Schottky diode structure., The ICN2 is funded by the CERCA programme/Generalitat de Catalunya and by the Severo Ochoa programme of the Spanish Ministry of Economy, Industry and Competitiveness (MINECO, Grant No. SEV-2017-0706). GEMaC colleagues acknowledge financial support of the French National Agency of Research (ANR), project “GOPOWER” (No. CE-50 N0015-01).