Vol 48, No 5 (2019)

The effect of technetium as a component of the catalysts of the REE compounds on the decomposition process of monohydrides of silicon and germanium during the growth of epitaxial films of silicon and a silicon-germanium solid solution is found. It is demonstrated that due to the fact that the unit cell radius of technetium is 1.358 Å and is similar to the unit cell radius of the platinum-group of elements (1.37–1.38 Å), as a component of the catalysts of REE compounds, it delivers the stability of the specific catalytic activity and high level of selectivity of the decomposition of monosilane and monogermane into \({\text{SiH}}_{2}^{{{\text{**}}}}\) and \({\text{GeH}}_{2}^{{{\text{**}}}}.\) This in turn allows carrying out the process of growing epitaxial films in two stages and, in this way, improving their quality.

At present, high-performance computations are in general use: in the government sector, defense sphere, large-scale concerns and enterprises, commercial entities, etc. From year-to-year, increasing sums are invested in supercomputers and software to rapidly answer any challenges in some field. In general, present-day high-performance computing systems are based on the von Neumann computation model. This, in turn, imposes certain restrictions, in particular, on paralleling computations and, as a consequence, on the creation of effective parallel applications. In this paper, one of the ways of solving this problem is proposed, namely, transition to the original dataflow computation model with a dynamically generated context and its implementation in a parallel dataflow computing system. Peculiarities of the parallel dataflow computing system architecture and the advantages offered by them are described. Such architectures will be in demand when creating new microelectronic high-performance computation systems.

Methods of machine learning are actively used to construct neuromorphic fault dictionaries that provide the fault diagnostics of analog and mixed-signal integrated circuits in an associative mode. Many problems of the neural network (NN) training associated with the large amount of input data can be solved by reducing the size of the training data sets and using only their significant characteristics. In this paper, a route for the formation of a neuromorphic fault dictionary (NFD) is presented, a method based on the calculation of the entropy for choosing the significant characteristics of the training set is proposed, and the corresponding algorithm is developed. The results of the experimental studies for analog filter are shown demonstrating high efficiency of the proposed method: reduction by a factor of 192 in the NN training time, and coverage up to 95.0% of catastrophic faults and up to 84.81% of parametric faults by the resulting NFD in the course of diagnostics.

The results of designing a thermoelectric generator (TEG) for low-power applications, such as human monitoring systems, are presented. The generator principle is based on using the Seebeck effect. We describe a manufacturing process and the results of testing a thin-film TEG with an output power of 3–56 μW and a temperature difference of 25–100 K.

During the pulse electric strength test of the electronic component base (ECB), the single pulsing electrical overstress (EOS) of increasing amplitude affects the tested specimen until a functional or parametric failure of the product appears. In order to reduce the test time and minimize operator intervention, a specialized test bench was designed to perform the test in automatic mode. To have the possibility to switch the specimen between a sensitive measuring instrument and a high-voltage pulse generator, a mechanical switching device was developed, which also operates as a connecting link between test bench components. After putting the test bench into operation, the time of preparation and the test significantly decreases. Furthermore, it became possible to test the electronic component base on the multiple actions of voltage pulses.