Department of Physics and Information Systems

Important scientific results obtained by the Department researchers:

1. The Department of Physics was the first to have synthesized semiconductors from the CdxHg1-xTe solid solution system.
2. For the first time, the phenomenon of negative photoconductivity in crystals of CdTe – ZnTe solid solutions was experimentally studied.
3. For the first time, the scientists established photoelectric state formation regularities under the influence of an external field and its destruction in high-resistance photosensitive semiconductors A2B6.
4. For the first time, magnetophonon resonance on the difference of phonons belonging to different modes of the crystal was experimentally detected in solid solutions of CdxHg1-xTe crystal.
5. Complex studies of physical properties of semiconductors ZnTe, ZnxHg1-xTe, ZnxCd1-xTe were synthesized and carried out
6. The regularities of the influence of electron-deformation effects on the optical and electrical properties of strained heterostructures with quantum dots have been established.
7. The staff have developed a model of the architecture of a modified stochastic fully connected neural network in the form of a Hessenberg neural network with diagonal synaptic connections between sensory neurons and computational stochastic pseudospin neurons and established the regularity of the change in the setting time of synaptic connections between stochastic pseudospin neurons in the Hessenberg neural network relative to the setting time of synaptic connections between stochastic pseudospin neurons in a fully connected neural network.
8. A three-layer artificial neural diagonalized network was developed for the approximation of two-dimensional functions. It is proved that the diagonalization operation can be used for simplified adjustment of the weight coefficients of synaptic connections in the neural network in the process of its learning due to changing the configuration of the network by excluding some existing connections between neurons and reducing the elements of the connection matrix between the vector of input signals and neurons.