Gallium oxide, and in particular its thermodynamically stable β-Ga2O3 phase, is within the most exciting materials in research and technology nowadays due to its unique properties. The very high breakdown electric field and the figure of merit rivaled only by diamond have tremendous potential for the next generation “green” electronics enabling efficient distribution, use, and conversion of electrical energy. Ion implantation is a traditional technological method used in these fields, and its well-known advantages can contribute greatly to the rapid development of physics and technology of Ga2O3-based materials and devices. Here, the status of ion implantation in β-Ga2O3 nowadays is reviewed. Attention is mainly paid to the results of experimental study of damage under ion irradiation and the properties of Ga2O3 layers doped by ion implantation. The results of ab initio theoretical calculations of the impurities and defect parameters are briefly presented, and the physical principles of a number of analytical methods used to study implanted gallium oxide layers are highlighted. The use of ion implantation in the development of Ga2O3-based devices, such as metal oxide field-effect transistors, Schottky barrier diodes, and solar-blind UV detectors, is described together with systematical analysis of the achieved values of their characteristics. Finally, the most important challenges to be overcome in this field of science and technology are discussed.

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO₂(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.