Effect of Post-Implantation Heat Treatment Conditions on Photoluminescent Properties of Ion-Synthesized Gallium Oxide Nanocrystals.

A novel and promising way for creating nanomaterials based on gallium oxide is the ion synthesis of Ga2O3 nanocrystals in a SiO2/Si dielectric matrix. The properties of nanocrystals are determined by the conditions of ion synthesis-the parameters of ion irradiation and post-implantation heat treatment. In this work, the light-emitting properties of Ga2O3 nanocrystals were studied depending on the temperature and annealing atmosphere. It was found that annealing at a temperature of 900 °C leads to the appearance of intense luminescence with a maximum at ~480 nm caused by the recombination of donor-acceptor pairs. An increase in luminescence intensity upon annealing in an oxidizing atmosphere is shown. Based on data from photoluminescence excitation spectroscopy and high-resolution transmission electron microscopy, a hypothesis about the possibility of the participation of a quantum-size effect during radiative recombination is proposed. A mechanism for the formation of Ga2O3 nanocrystals during ion synthesis is suggested, which makes it possible to describe the change in the luminescent properties of the synthesized samples with varying conditions of post-implantation heat treatment.

Electrical Characteristics of CMOS-Compatible SiOx-Based Resistive-Switching Devices.

The electrical characteristics and resistive switching properties of memristive devices have been studied in a wide temperature range. The insulator and electrode materials of these devices (silicon oxide and titanium nitride, respectively) are fully compatible with conventional complementary metal-oxide-semiconductor (CMOS) fabrication processes. Silicon oxide is also obtained through the low-temperature chemical vapor deposition method. It is revealed that the as-fabricated devices do not require electroforming but their resistance state cannot be stored before thermal treatment. After the thermal treatment, the devices exhibit bipolar-type resistive switching with synaptic behavior. The conduction mechanisms in the device stack are associated with the effect of traps in the insulator, which form filaments in the places where the electric field is concentrated. The filaments shortcut the capacitance of the stack to different degrees in the high-resistance state (HRS) and in the low-resistance state (LRS). As a result, the electron transport possesses an activation nature with relatively low values of activation energy in an HRS. On the contrary, Ohm's law and tunneling are observed in an LRS. CMOS-compatible materials and low-temperature fabrication techniques enable the easy integration of the studied resistive-switching devices with traditional analog-digital circuits to implement new-generation hardware neuromorphic systems.

Model of Neuromorphic Odorant-Recognition Network.

We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of "decoder" neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the "decoder" layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of "decoder" neurons does not exhibit specificity in recognizing odorants. The recognition of the 'odorant' occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications.

Structure and Chemical Composition of Ion-Synthesized Gallium Oxide Nanocrystals in Dielectric Matrices.

The ion-beam synthesis of Ga2O3 nanocrystals in dielectric matrices on silicon is a novel and promising way for creating nanomaterials based on gallium oxide. This research studies the regularities of changes, depending on the synthesis regimes used, in the chemical composition of ion-implanted SiO2/Si and Al2O3/Si samples. It has been shown that the formation of Ga-O chemical bonds occurs even in the absence of thermal annealing. We also found the conditions of ion irradiation and annealing at which the content of oxidized gallium in the stochiometric state of Ga2O3 exceeds 90%. For this structure, the formation of Ga2O3 nanocrystalline inclusions was confirmed by transmission electron microscopy.

Memristive Switching and Density-Functional Theory Calculations in Double Nitride Insulating Layers.

In this paper, we demonstrate a device using a Ni/SiN/BN/p+-Si structure with improved performance in terms of a good ON/OFF ratio, excellent stability, and low power consumption when compared with single-layer Ni/SiN/p+-Si and Ni/BN/p+-Si devices. Its switching mechanism can be explained by trapping and de-trapping via nitride-related vacancies. We also reveal how higher nonlinearity and rectification ratio in a bilayer device is beneficial for enlarging the read margin in a cross-point array structure. In addition, we conduct a theoretical investigation for the interface charge accumulation/depletion in the SiN/BN layers that are responsible for defect creation at the interface and how this accounts for the improved switching characteristics.

Ion-Beam Synthesis of Gallium Oxide Nanocrystals in a SiO2/Si Dielectric Matrix.

A new method for creating nanomaterials based on gallium oxide by ion-beam synthesis of nanocrystals of this compound in a SiO2/Si dielectric matrix has been proposed. The influence of the order of irradiation with ions of phase-forming elements (gallium and oxygen) on the chemical composition of implanted layers is reported. The separation of gallium profiles in the elemental and oxidized states is shown, even in the absence of post-implantation annealing. As a result of annealing, blue photoluminescence, associated with the recombination of donor-acceptor pairs (DAP) in Ga2O3 nanocrystals, appears in the spectrum. The structural characterization by transmission electron microscopy confirms the formation of ß-Ga2O3 nanocrystals. The obtained results open up the possibility of using nanocrystalline gallium oxide inclusions in traditional CMOS technology.

Stochastic Memristive Interface for Neural Signal Processing.

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.

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot.

Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.