Precision technological processes for the production of modern microelectronic products require compliance with the quality of source materials, working environments, and precise adherence to regimes. Due to natural fluctuations in the properties of materials and the environment, and variable states of all technological processes, the parameters of the product and technological processes cannot be described by deterministic laws. Due to the inevitable natural properties of fluctuations in the parameters of technological equipment and its operating modes, the state variables of all technological processes are random functions of space-time coordinates. In most cases, these accidents cannot be neglected, since they all affect the output parameters of the products. The most complex mathematical models of technological systems and processes in modern theory are random spatiotemporal fields, representing both input and output characteristics, as well as parameters of the systems under consideration. The purpose of this work is to model real-valued values of correlation functions of non-stationary random processes and sequences. When constructing a correlation theory of random processes and sequences, a complex representation is widely used, i.e. random functions of the form are considered: continuous or discrete time. This approach made it possible to construct a correlation theory of nonstationary random functions using the spectral theory of non-self-adjoint or unitary operators and to introduce the concept of complex spectrum. For applications of the correlation theory of nonstationary random functions and their modeling, it is convenient to deal with real-valued correlation functions. The construction of real-valued correlation functions can be carried out using the well-known fact that the real part of complex-valued correlation functions is also a correlation function (for the imaginary part this statement is unfair, since the imaginary part is a cross-correlation function of the real and imaginary parts of the corresponding random process or sequence) . The resulting models of correlation functions of non-stationary random processes and sequences can be used to construct algorithms for forecasting and filtering non-stationary random functions
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