Dmitry Korolev

Bio: Dmitry Korolev is an academic researcher from N. I. Lobachevsky State University of Nizhny Novgorod. The author has contributed to research in topics: Ion implantation & Silicon. The author has an hindex of 5, co-authored 23 publications receiving 183 citations.

Bipolar resistive switching and charge transport in silicon oxide memristor

Abstract: Reproducible bipolar resistive switching has been studied in SiO x -based thin-film memristor structures deposited by magnetron sputtering technique on the TiN/Ti metalized SiO 2 /Si substrates. It is established that, after electroforming, the structure can be switched between the quasi-ohmic low-resistance state related to silicon chains (conducting filaments) and the high-resistance state with semiconductor-like hopping mechanism of charge transport through the defects in silicon oxide. The switching parameters are determined by a balance between the reduction and oxidation processes that, in turn, are driven by the value and polarity of voltage bias, current, temperature and device environment. The results can be used for the development of silicon-based nonvolatile memory and memristive systems as a key component of future electronics.

Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware

Abstract: Building bidirectional biointerfaces is one of the key challenges of modern engineering and medicine, with dramatic potential impact on bioprosthetics. Two of the major challenges of biointerface design concern signal stability and power efficiency. The former entails: a) ensuring that biosignal inputs corresponding to the same ground truth (e.g. patient “intentions”) are recorded and interpreted consistently and b) maintaining the mapping from biointerface stimulation outputs to behavioral outputs (e.g. muscle movements). In this work we demonstrate how machine learning techniques, state-of-art nanoelectronics and microfluidics can combine forces to build and test low-power, adaptable biointerfaces that address both key challenges. Specifically, we demonstrate that: 1) we can emulate the input/output transfer characteristics of a structure biological neural network (BNN) with an artificial one (ANN), 2) it is possible to translate the resulting, “ideally trained” ANN into a hardware network using RRAM devices as synapses without significant loss of accuracy, despite concerns in the community about RRAM device reliability and 3) using a very simple mechanism of shifting the active stimulation electrode can fully restore functionality after the initial stimulation site degrades, prolonging the usable lifetime of the biointerface significantly. In this manner we place a key stepping stone towards building self-adjusting, low-power biointerfaces, themselves a foundational stepping stone towards adaptable, low-power bioprostheses.

Ion implantation in β-Ga2O3: Physics and technology

Abstract: 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

One-Board Design and Simulation of Double-Layer Perceptron Based on Metal-Oxide Memristive Nanostructures

Abstract: Design and training principles have been proposed and tested for an artificial neural network based on metal-oxide thin-film nanostructures possessing bipolar resistive switching (memristive) effect. Experimental electronic circuit of neural network is implemented as a double-layer perceptron with a weight matrix composed of 32 memristive devices. The network training algorithm takes into account technological variations of the parameters of memristive devices. Despite the limited size of weight matrix the developed neural network model is scalable and capable of solving nonlinear classification problems. The learning and functionality of the network are demonstrated by using its computer model for the classification of activity propagation directions in simulated neuronal culture.

Silicon Oxide (SiOx ): A Promising Material for Resistance Switching?

Abstract: Interest in resistance switching is currently growing apace. The promise of novel high-density, low-power, high-speed nonvolatile memory devices is appealing enough, but beyond that there are exciting future possibilities for applications in hardware acceleration for machine learning and artificial intelligence, and for neuromorphic computing. A very wide range of material systems exhibit resistance switching, a number of which-primarily transition metal oxides-are currently being investigated as complementary metal-oxide-semiconductor (CMOS)-compatible technologies. Here, the case is made for silicon oxide, perhaps the most CMOS-compatible dielectric, yet one that has had comparatively little attention as a resistance-switching material. Herein, a taxonomy of switching mechanisms in silicon oxide is presented, and the current state of the art in modeling, understanding fundamental switching mechanisms, and exciting device applications is summarized. In conclusion, silicon oxide is an excellent choice for resistance-switching technologies, offering a number of compelling advantages over competing material systems.

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.

Abstract: Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

Stochastic resonance in a metal-oxide memristive device

Abstract: The stochastic resonance phenomenon has been studied experimentally and theoretically for a state-of-art metal-oxide memristive device based on yttria-stabilized zirconium dioxide and tantalum pentoxide, which exhibits bipolar filamentary resistive switching of anionic type The effect of white Gaussian noise superimposed on the sub-threshold sinusoidal driving signal is analyzed through the time series statistics of the resistive switching parameters, the spectral response to a periodic perturbation and the signal-to-noise ratio at the output of the nonlinear system The stabilized resistive switching and the increased memristance response are revealed in the observed regularities at an optimal noise intensity corresponding to the stochastic resonance phenomenon and interpreted using a stochastic memristor model taking into account an external noise source added to the control voltage The obtained results clearly show that noise and fluctuations can play a constructive role in nonlinear memristive systems far from equilibrium

Measurement of the activation energies of oxygen ion diffusion in yttria stabilized zirconia by flicker noise spectroscopy

Abstract: The low-frequency noise in a nanometer-sized virtual memristor consisting of a contact of a conductive atomic force microscope (CAFM) probe to an yttria stabilized zirconia (YSZ) thin film deposited on a conductive substrate is investigated. YSZ is a promising material for the memristor application since it is featured by high oxygen ion mobility, and the oxygen vacancy concentration in YSZ can be controlled by varying the molar fraction of the stabilizing yttrium oxide. Due to the low diameter of the CAFM probe contact to the YSZ film (∼10 nm), we are able to measure the electric current flowing through an individual filament both in the low resistive state (LRS) and in the high resistive state (HRS) of the memristor. Probability density functions (Pdfs) and spectra of the CAFM probe current in both LRS and HRS are measured. The noise in the HRS is found to be featured by nearly the same Pdf and spectrum as the inner noise of the experimental setup. In the LRS, a flicker noise 1/fγ with γ ≈ 1.3 is observed in the low-frequency band (up to 8 kHz), which is attributed to the motion (drift/diffusion) of oxygen ions via oxygen vacancies in the filament. Activation energies of oxygen ion motion determined from the flicker noise spectra are distributed in the range of [0.52; 0.68] eV at 300 K. Knowing these values is of key importance for understanding the mechanisms of the resistive switching in YSZ based memristors as well as for the numerical simulations of memristor devices.

Superior resistive switching memory and biological synapse properties based on a simple TiN/SiO2/p-Si tunneling junction structure

Abstract: In this study, a simple TiN/SiO2/p-Si tunneling junction structure was fabricated via thermal oxidation growth on a Si substrate annealed at 600 °C. After electroforming, the number of cycle times for the SiO2-based tunneling junction device can reach an order of magnitude of greater than 105. The resistances at low and high resistance states and the threshold voltage of the device fluctuated in a very narrow range. More interestingly, excitatory and inhibitory postsynaptic current phenomena (EPSC and IPSC) were observed during the pulse mode measurements, indicating that the device can be used in biological synapse applications. At different measurement temperatures and electric fields, direct, Fowler–Nordheim, and trap-assisted tunneling were responsible for the intrinsic conductance mechanism of the device before and after electroforming. This study provides a convenient approach to prepare simple tunneling junction structures for resistive random access memory applications with superior properties.