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• HARDWARE NEURAL NETWORK BASED MEMRISTIVE TITANIUM OXIDE STRUCTURES

  V.I. Avilov , L. А. Dushina , N.V. Polupanov , V. А. Smirnov

  205-214

  2025-11-10

  Abstract ▼

  The paper presents the results of manufacturing, training and research of a hardware neural network prototype implemented as a crossbar array of artificial synapses based on memristor nanostructures of electrochemical titanium oxide. A prototype of a fully connected neural network was developed, consisting of four input electrodes, a crossbar array of 16 artificial synapses based on electrochemical titanium oxide nanostructures and four output electrodes. It is shown that the process of current flow through such a structure fully corresponds to the mathematical model of the neural network. Various implementations of artificial synapses that allow the implementation of negative "weights" of the neural network were analyzed and one of the optimal options was selected. Based on the developed structure, a prototype of a fully connected neural network was manufactured using magnetron sputtering, optical lithography and nanolithography technologies using scanning probe microscopy methods. To train the neural network, an algorithm for switching individual memristors was developed, eliminating parasitic switching of neighboring structures due to the occurrence of leakage current. To demonstrate the operation of the manufactured neural network model, a task of classifying two input signals was proposed. To implement negative "weights", each of the incoming signals was duplicated with negative polarity. It is assumed that the outputs of the trained neural network should register: 1) the excess of the first signal; 2) the excess of the second signal; 3) both high signals. The training and research of the neural network was carried out using the hardware and software complex "Neuro InT", developed by the staff of the Research Laboratory "Neuroelectronics and Memristive Nanomaterials", SFedU. Research of the neural network model showed that all outputs successfully classify incoming signals, maximizing the current through the corresponding outputs for the given input values. The proposed structure can be improved by adding two additional inputs with a constant high positive and negative potential to implement a "shift" during the operation of the neural network. The obtained results can be used in the development of technological foundations for the formation of hardware neural networks based on memristor titanium oxide nanostructures

• FORMATION AND INVESTIGATION OF DOPED ZINC OXIDE MEMRISTIVE FILMS FOR MACHINE VISION SYSTEMS OF ROBOTIC COMPLEXES

  Z. Е. Vakulov , R.V. Tominov , Д.A. Dzyuba , V.А. Smirnov

  116-123

  2025-11-10

  Abstract ▼

  The results of investigation of the influence of synthesis modes of doped zinc oxide thin films by pulsed laser deposition on their morphological and electrophysical characteristics are presented. Experimental studies of the influence of dimensional effects on the parameters of resistive switching of memristor structures based on thin films of doped zinc oxide have been carried out. The relationship between the morphological parameters of the films, their thickness and resistive switching characteristics has been established. The results showing how thickness, surface roughness and average grain diameter influence the ratio of resistance in the high-resistance and low-resistance states, as well as the switching voltages U_set and U_res have been obtained. It is shown that an increase in the thickness of gallium-doped zinc oxide films leads to an increase in the Uset and Ures voltages, while the dependence of the resistance ratio in the high-resistance and low-resistance states has a complex character, with a maximum observed at a film thickness of about 30 nm. The obtained results allow us to estimate the degree of influence of structural and morphological parameters of doped zinc oxide films on the resistive switching effect in them, and also to formulate recommendations for obtaining these films with the required resistive switching parameters. It was found that increasing the thickness of gallium-doped zinc oxide films from 11.8±5.1 nm to 55.1±18.4 nm it is possible to change the value of charge carriers concentration from (2.84±0.22)∙1019 cm-3 to (1.42±0.13)∙10²⁰ cm^-3, as well as the mobility of charge carriers from 54.48±4.07 cm²/(V∙s) to 18.77±0.83 cm²/(V∙s). At the same time, increasing the thickness of gallium-doped zinc oxide films also leads to an increase in resistance in the high-resistance state from 1.38±0.11 MΩ to 62.59±5.4 MΩ and resistance in the low-resistance state from 0.005±0.001 MΩ to 0.041±0.002 MΩ. The results obtained can be used in the development of physical principles of creation of electronic component base of artificial intelligence systems for manufacturing new devices and devices of nanoelectronics and adaptive neuromorphic systems