Predictional mathematical models of thin-film elements of microassembly

DOI: 10.34759/trd-2023-131-14

Аuthors

Piganov M. N.^*, Kulikov A. V.^**, Novomeisky D. N.^***

Samara National Research University named after Academician S.P. Korolev, 34, Moskovskoye shosse, Samara, 443086, Russia

*e-mail: kipres@ssau.ru
**e-mail: avksam@mail.ru
***e-mail: dmitr.novomejscky@yandex.ru

Abstract

The lion share of the onboard equipment failures (up to 70%) is stipulated by the inadequate reliability of the onboard equipment electronic parts base.

One of the effective ways to improve the onboard radio-electronic systems quality consists in selecting highly reliable elements and components based on the results of diagnostic control or individual prediction (IP) of their future state. This is especially true for the devices and elements, such as thin-film microassemblies with high-precision resistors and capacitors.

One of the most important operations in such microassemblies manufacturing is the thin-film elements trimming to the nominal value. However, this trimming operation often introduces a perturbing effect into their structure, which reduces the temporal stability and reliability of these elements. This makes the procedure for rejecting potentially unreliable elements and selecting high-quality samples for the onboard equipment (the IP based) especially up-to-date. Mathematical models are proposed for individual prediction of quality and reliability indicators of microassembly thin-film resistors and capacitors.

An expert survey was conducted to select the forecasting method. With account fort the fact that informative parameters were previously identified for the class of microassemblies and thin-film elements under study, the following methods of pattern recognition theory were selected as the main ones: the method of discriminant functions, potential and regression functions (models), as well as the extrapolation method. The latter was emplooyed for the case of low information content of the parameters.

Forecasting effectiveness estimate based on the proposed models is presented. The developed models are recommended to be applied for solving problems of individual prediction of elements with classification.

Keywords:

predictive mathematical model, prediction, efficiency, analysis of thin film resistors, capacitor, microassembly, on-board device

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