A technique has been developed for frequency analysis of the IR reflection spectrum to determine the thickness and order of the layers in the epitaxial structure of silicon carbide. Calculations for the 4H-SiC epitaxial structure have been performed. The method has been shown to be highly sensitive to optical boundaries resulting from a sequential increase in the doping level during the layer growth. Keywords: Silicon carbide, epitaxial layer, IR reflection, spectrum

In this work a method for increasing the efficiency of hardware implementation and minimizing the number of electronic synaptic elements of asynchronous spiking neural networks in solving image identification problems is developed. The effectiveness of the proposed method is demonstrated in the process of optimizing the parameters of neurons and training the neural network on the developed software model and confirmed by the results of SPICE modeling and measurement of the signals of the neural network implemented on series electronic components.

The possibility to increase the responsivity of 4H-SiC p+-n-n+-photodiodes by varying the thickness of the p+-epilayer has been studied. It is shown that the thinning of the upper epilayer by RIE with the use of metal contacts as a mask makes it possible to control both the maximum responsivity and the spectral dependence of the responsivity of photodiodes and does not lead to degradation of dark electrical characteristics. Keywords: 4H-SiC, p+-n-n+-photodiode, UV-range, p+-epilayer, reactive ion etching RIE, responsivity.

A sensor glove was developed to control a manipulator in the form of a human hand, which could remotely repeat the movements of the operator's fingers, as well as capture and hold objects of various sizes and shapes. Ionic electroactive polymers with hybrid electrodes were used as sensors that ensured the operation of the system. Such structures had greater durability and were characterized by a stable change in resistance during bending.

A physical and technological analysis of silicon-carbide epitaxial structures as the basic components of betavoltaic converters (BVC) is carried out. The main factors limiting the efficiency of SiC-BVCs are determined. It is shown that in order to provide the required level of electric power for an actually long-term inexhaustible energy source, it is necessary to use series-parallel hybrid circuits of large-area betavoltaic multichip converters.

Ion implantation is a key technology without alternative for doping silicon carbide SiC in the manufacturing processes of SiC devices. SiC technology has a number of distinctive features in comparison with Si-ion doping technology. This paper provides a systematic analysis of modern technical solutions aimed at the formation of local doped regions by the method of ion implantation for various purposes for SiC-based high-power electronic devices. The results of research conducted at the St. Petersburg State Electrotechnical University LETI are presented. This research is focused on the development and selection of modes of aluminum- and phosphorus-ion implantation into 4H-SiC structures that provide specified concentrations of doping impurities and geometric dimensions of local ion-doped regions. The developed ion-implantation modes are successfully implemented in the manufacture of samples of high-power 4H-SiC metal–insulator–semiconductor (MIS) transistors with operating voltages of up to 1200 V.

The paper presents the hardware architecture design of a spiking neural network (SNN) based on dendritic computation principles. The integration of active dendritic properties into the neuronal structure of SNN aims to minimize the number of functional blocks required for hardware implementation, including synaptic connections and neurons. The available memory on the neuromorphic architecture imposes limitations on implementation, hence the need to reduce the number of functional blocks. As a test task for the SNN based on dendritic computations, we selected the image classification of eight symbols, consisting of digits one through eight. These symbols are depicted as 3×7 pixel, 1-bit images. Active dendritic properties were analyzed using the “delay plasticity” [1] principle, which introduces the mechanism of adjusting input signal delays in spiking neuron inputs. We designed an SNN model with complementary delay inputs, referred to as the active dendrite SNN, as a principle implementation. Input spikes arriving at the primary inputs are duplicated to the delay inputs after a modifiable time delay. For convenience, each delay input was set at a single value. The input images were scanned sequentially. The neural network received three direct and three inverse inputs from the six main inputs that were coded with spikes corresponding to three pixels of a string. An “on” pixel was coded with a spike arriving at a direct input, while an “off” pixel was coded with a spike arriving at the corresponding inverse input. The line scanning time was 10 μs, input width was 1 μs, and delay time was 5 μs. The optimization of spiking neuron parameters was performed through a stochastic search algorithm based on simulated annealing. The parameters optimized for the Leaky-Integrate-and-Fire (LIF) neurons included the leakage time constant (22.8 μs), firing threshold (1150 arbitrary units), and refractory period (1 μs). The active dendrite SNN training employed the tempotron learning rule [2]. The training optimized the following parameters: the maximum change in synaptic weight on potentiation and depression (0.7 and –3 arbitrary units, respectively) and the synaptic weight’s upper bound (195 arbitrary units). Complementary delayed inputs facilitated the learning of the order in which input patterns arrived for SNN neurons during training. The paper compares an SNN architecture based on dendritic computations to our previously designed two-layer SNN with a hidden perceptron layer and an output layer consisting of LIF neurons [3]. Using the same LIF neuron design, input image coding, and LIF neuron layer structure as in the proposed architecture, our two-layer SNN with a hidden perceptron layer and output layer of LIF neurons successfully recognized 3×5 images of three symbols with only 10 neurons and 63 synapses. Alternatively, the active dendrite SNN was able to recognize 3×7 images of eight symbols with four neurons and 48 synaptic weights. In conclusion, incorporating active dendrite properties into the SNN architecture for image recognition resulted in optimized functional block usage, lowering the number of neurons and synapses used by 60 and 24%, respectively.

The constructive and technological solutions of a new-generation interactive multimodal hybrid conformal sensor-correcting microsystem are presented. Functional modules of the microsystem made in the form of an ultrathin bracelet or patch with the possibility of being fixed to human skin are considered. The advantages of the proposed microsystem, its purpose and possible applications are discussed.

One of the promising approaches to the creation of a new electronic-component base is the use of multilevel neuromorphic logic structures based on thin film memristive compositions. Experimental prototypes of memristive synapses are implemented. Memristive devices with the effects of multilevel resistance switching based on heterogeneous (consisting of a sequence of dielectric layers) thin-film structures providing analog (multilevel) restructuring between energy-independent states in terms of resistance in the range of seven orders of magnitude are developed. Possible variants of circuit solutions for neuromorphic modules are presented.

In the current era, the design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks (ANN) effectively solve a wide range of common artificial-intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of the behavior of an object. The biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to large parallel and asynchronous data processing. In this paper, we present a circuit design of main functional blocks (neurons and synapses) intended for the hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timed spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approaches to emulating synaptic behavior in hardware implemented spiking neural networks is to use nonvolatile-memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structures to mimic synaptic plasticity, pre- and postsynaptic neurons which could be used for developing a circuit design of spiking-neural-network architectures with different training algorithms including the spike-timing-dependent-plasticity learning rule. Two different circuits of an electrical synapse are developed. The first one is an analog synapse with a photoresistor optocoupler used to ensure the tunable conductivity for synaptic-plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without a digital-to-analog converter and photoresistor optocoupler). The results of prototyping the developed circuits for electronic analogs of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for the ASIC (application-specific integrated circuit) design of spiking neural networks based on CMOS (complementary metal—oxide–semiconductor) design technology.

The creation of multilayer resistive memory devices based on nanolayer memristive compositions with thin ferroelectric (FE) films, in which resistance switching is caused by a combination of effects related to the influence of interface regions, polarization states, charge transport mechanisms, and microscopic features of nanostructures, requires the development of new experimental approaches to the study of local electrophysical properties. One of the most common ways to investigate local electrophysical properties is the use of various atomic force microscopy (AFM) techniques, including the Kelvin probe microscopy, tunneling AFM, and piezoresponse AFM. The main reason for switching from AFM to scanning tunneling microscopy techniques when studying the local resistive properties of memristive compositions with FE films is the need to stabilize the probe-sample contact. The main obstacle to the effective use of AFM techniques to study the local ferroelectric properties in nanolayer memristive compositions with thin FE films is the occurrence of a strain gradient during scanning, which leads to the contribution of the flexoelectric effect and direct piezoelectric effect in the measurement results. In this work, we have developed a method of investigating the local FEproperties using scanning tunneling microscopy (STM) and spectroscopy (STS) techniques under ultra-high vacuum conditions. The essence of the proposed approach is to identify the contribution of polarization charges, as well as the features of their screening on the free surface of the FE film, to the results of STS measurements at different polarization orientations in the FE film. In combination with STM measurements of local morphological features, the analysis of experimental results makes it possible to identify the state of FE polarization, determine the contribution of the surface screening of polarization charges to the manifestation of memristive effects, and investigate the correlation between the local resistive and FE properties in nanolayer memristive compositions with thin FE films.

Silicon carbide SiC is the basic material of modern extreme and power electronics. The characterization of substrates and SiC-based epitaxial multilayers requires advanced on-line control methods to be developed. In this study, contactless nondestructive optical methods for controlling the kinetic parameters of carriers, layer thickness, and surface quality are demonstrated. Techniques based on the physical processes of the interaction between a photon flux and a SiC single crystal are used. The carrier density and mobility and layer thicknesses in epitaxial multilayers are determined and the substrate surface-treatment quality is characterized by approximating infrared reflectance spectra. The data obtained are verified by independent methods. The proposed algorithm for combined use of these methods ensures the efficient control of substrates and epitaxial compositions along with reproducible characteristics and functional parameters.

Heterolayer memristive systems with a functional aluminum oxide layer obtained by the method of atomic layer deposition are studied. A method is proposed for the arrangement of a reservoir of oxygen vacancies, which ensures the creation of a wide memory window. The proposed method uses oxygen permeable platinum electrodes in combination with an amorphous-silicon-dioxide adsorption sublayer formed by plasma-chemical deposition under the bottom electrode of the structure. It is shown that an amorphous-silicon-dioxide sublayer can play, under certain conditions, the role of a reservoir of oxygen vacancies for the functional sublayer, which provides a reversible change in the concentration of molecular oxygen in the regions adjacent to the electrodes and reversible readjustment of the resistivity of the structure within the range of seven orders of magnitude.

The developed and implemented complex of miniature bionic robotic systems is presented, including swimming and walking miniature biosimilar robots with low-voltage energy-efficient motion drives based on ionic polymer-metal composites; miniature autonomous robotized probes with a vibration propulsor for moving through small-diameter pipes and transmitting a video signal over a two-frequency radio channel; an ultralight zoomorphic flying platform with a movable wing — a robot ornithopter "Hummingbird".

The results of studies on the development of technological methods for the formation of low-resistivity contact systems to n- and p-SiC based on single and multilayer Ni-, Al- and Ti-compositions for 4H-SiC based bipolar devices are presented. It is shown that the formation of low-resistivity contacts based on Ni to n-4H-SiC (rho c =3.6·10 -4 Ohm·cm 2 ) and Ni/Al to p-4H-SiC (rho c =5.9·10 -5 Ohm·cm 2 ) is possible within a single cycle of vacuum annealing at 1000 o C for 120 s. This technological solution makes it possible to reduce the number of high-temperature processes. Keywords: 4H-SiC, n-type, p-type, ohmic contacts, RTA, TLM, specific contact resistivity.

The paper proposes physical-technological solutions that provide passive protection of miniature wearable objects of the bio-and technosphere from the effects of electromagnetic radiation in the frequency range from 50 MHz to 18 GHz. Various conformal micro- and nanocompositions of electromagnetic shields and electromagnetic wave absorbers have been proposed and studied.

The results of studies on the development of technological methods for the formation of low-resistivity contact systems to n- and p-SiC based on single and multilayer Ni-, Al- and Ti-compositions for 4H-SiC based bipolar devices are presented. It is shown that the formation of low-resistivity contacts based on Ni to n-4H-SiC (ρс = 3.6∙10-4 Ω∙cm2) and Ni/Al to p-4H-SiC (ρс = 5.9 ∙10-5 Ω∙cm2) is possible within a single cycle of vacuum annealing at 1000 °C for 120 s. This technological solution makes it possible to reduce the number of high-temperature processes.

Currently, the expansion of the range of tasks solved by neural networks occurs mainly due to the complication of their structure, an increase in the number of neurons and synapses. Networks with thousands of neurons and tens to hundreds of thousands of synapses can achieve impressive results in the areas of speech processing, image identification, computer vision, etc., but at the same time, their use and training require significant computing power and energy costs. This is unacceptable for embedded autonomous systems, which, as follows from current trends, will constitute one of the most important applications for neural networks on a chip and must perform their tasks using a minimum number of elements and with minimal energy consumption. In this article a method for increasing the efficiency of hardware implementation and minimizing the number of electronic synaptic elements of asynchronous spiking neural networks (ASNN) in solving image identification problems was developed. To achieve this goal, a theoretical analysis of ASNN architectural solutions was carried out, the number of electronic synaptic elements was minimized due to the decomposition of the image identification problem, an ASNN software model was developed, neuron parameters were optimized and the neural network was learned (the weights of electronic synaptic elements were sat) on a software model. An ASNN electrical circuit was developed and the results of SPICE modeling were obtained. A hardware implementation of an asynchronous spiking neural network on serial electronic components was performed. The effectiveness of the proposed method was demonstrated in the process of optimizing the parameters of neurons and learning the neural network on the developed software model and confirmed by the results of SPICE modeling of the developed electrical circuit of the ASNN and the results of measuring the signals of the neural network implemented on serial electronic components.

The technology of inkjet printing for the formation of multilayer flexible commutation boards is presented. The technology improves the ergonomics of the electronic devices around a person, making it possible to create products of a new generation: "smart clothes", "smart skin" and "Laboratories on a Chip". One of the main advantages and features of flexible printed electronics is the ability to use organic polymers as a substrate material to provide conformal hybrid integration.