Bis(trimethylsilyl)ethylamine (BTMSEA) was synthesized and characterized as CVD precursor for silicon carbonitride SiC x N y films synthesis (vapor pressure, thermodynamic modeling). SiC x Ny films were deposited by PECVD from BTMSEA in the temperature range of 100-700 o C using two additional gases (He or NH 3 ). FT-IR, Raman spectroscopy, ellipsometry, EDX, SEM, UV-Visible spectroscopy and nanoindentation tests were used for film characterization. FT-IR analysis showed that temperature increase lead to the transition from a low-temperature polymeric-like films to the high-temperature inorganic material. It was also shown that the high-temperature films content predominantly Si-C bonds independently on the additional gas type. As it was confirmed by Raman spectroscopy, hightemperature SiC x N y films content carbon phase. Ammonia addition into the reaction mixture resulted in the shift of the temperature boundary of carbon phase-free region. The transmittance of SiC x N y films obtained using BTMSEA + He mixture in the deposition temperature range of 100-500 o C was 85-95 % and decreased significantly in the case of carbon phase formation at T dep more than 500 o C. Optical band gap estimated from UV-Vis spectra varied in the range of 1.9-4.4 eV depending on the deposition temperature. NH 3 addition to initial mixture led to the film transmittance decrease to 80-90 %, the optical band gap changed in the range of 2.0-5.1 eV. Nanoindentation tests showed that hardness of the films synthesized at high temperature was 18.5-21.5 GPa.
The excellent transparent in wide region of spectra, nanocomposite SiCxNy:H films were synthesized by RPECVD using hexamethylcyclotrisilazane in mixture of helium and nitrogen in the temperature range of 373-1073 K. The analysis of FTIR, XPS and Raman spectroscopy results showed that low temperature films are hydrogenated silicon oxycarbonitride. There are the formation of chemical bonding between Si, C, N atoms with predominate surrounding of Si atoms by nitrogen atoms and the absence of hydrogenous bonds in high temperature films. C-V and I-V measurements showed that SiCxNy:H films are low-k dielectrics. Thermal annealing of these films leads to their densifying, ordering of structure and increase of nanocrystals' size.
Thin boron carbonitride films were grown by plasma enhanced chemical vapour deposition using mixture of N-trimethylborazine with ammonia or hydrogen. The thermodynamic analysis of the chemical vapour deposition in B-C-N-H system was carried out for temperatures from 300 to 1300 K, a total pressure from 1.33 to 1333 Pa and a wide range of values of H2:B3N3H3(CH3)3 and NH3:B3N3H3(CH3)3 ratios. The effect of the gas phase composition and substrate temperature on the chemical composition and optical properties of the deposits was studied by ellipsometry, scanning electron microscopy, IR-spectroscopy, and optical transmittance spectrophotometry. The low temperature boron carbonitride films were demonstrated to exhibit high optical transparency in the region of 400-2000 nm (transmittance up to 93 %).
To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.
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