The superconducting sigma neuron is a single-junction interferometer, with a part of the circuit shunted by an additional inductance, which is also used to generate the output signal. It was previously predicted that the transfer function of this device would be close to the sigmoidal one at a certain relation between the values of inductance of its parts. This interferometer can be fabricated as a multilayer thin-film structure over a superconducting screen, which allows measuring the output magnetic flux in a single element. An analysis of the experimental data showed that the use of a superconducting screen did not ensure complete independence of the sigma neuron elements, as was assumed in the theoretical model. This paper presents a generalized model of the stationary state of a sigma neuron that takes into account the interaction between all its parts, including the input and readout elements.

The voltage–current characteristics of planar Pd0.99Fe0.01–Nb–Pd0.99Fe0.01 microbridges at temperatures significantly lower than the critical one are studied experimentally. It has been found that a magnetic memory effect, which is manifested in the dependence of the shape of the voltage–current characteristics on the mutual orientation of the magnetizations of the F layers, is observed even at such low temperatures. It has been shown that the studied sample can serve as a magnetic switch with a voltage distinction of more than 600 μV, which corresponds to a characteristic frequency of about 300 GHz if such bridges are used as memory elements in rapid single-flux quantum logic devices. These characteristics are obtained at a temperature of 0.93Tc, which is the minimum operating temperature of the implemented memory element. A low-voltage mode of operation of the sample is discovered, characterized by a wide range of permissible bias currents.

The artificial neuron proposed earlier for use in superconducting neural networks is experimentally studied. The fabricated sample is a single-junction interferometer, part of the circuit of which is shunted by an additional inductance, which is also used to generate an output signal. A technological process has been developed and tested to fabricate a neuron in the form of a multilayer thin-film structure over a thick superconducting screen. The transfer function of the fabricated sample, which contains sigmoid and linear components, is experimentally measured. A theoretical model is developed to describe the relation between input and output signals in a practical superconducting neuron. The derived equations are shown to approximate experimental curves at a high level of accuracy. The linear component of the transfer function is shown to be related to the direct transmission of an input signal to a measuring circuit. Possible ways for improving the design of the sigma neuron are considered.

The transfer function of a shunted two-junction interferometer, which was previously proposed as a basic element of superconducting neural networks based on radial basis functions, has been measured for the first time. The sample has been implemented in the form of a multilayer thin-film structure over a thick superconducting screen with the inductive supply of an input signal and the readout of an output signal. It has been found that the transfer function is the sum of the linear and periodic bell-shaped components. The linear component is likely due to the direct transfer of the input magnetic flux to the measuring circuit. The shape of the nonlinear component, which is the output signal of a Gauss neuron, can be approximately described by a Gaussian distribution function or, more precisely, by a parametric dependence derived theoretically in previous works. It has been shown that the transfer function of the Gauss neuron can depend on the choice of the working point of the measuring circuit, which promotes the development of integrated neural networks based on implemented elements.

The transfer function of a shunted two-junction interferometer, which was previously proposed as a basic element of superconducting neural networks based on radial basis functions, has been measured for the first time. The sample has been implemented in the form of a multilayer thin-film structure over a thick superconducting screen with the inductive supply of an input signal and the readout of an output signal. It has been found that the transfer function is the sum of the linear and periodic bell-shaped components. The linear component is likely due to the direct transfer of the input magnetic flux to the measuring circuit. The shape of the nonlinear component, which is the output signal of a Gauss neuron, can be approximately described by a Gaussian distribution function or, more precisely, by a parametric dependence derived theoretically in previous works. It has been shown that the transfer function of the Gauss neuron can depend on the choice of the working point of the measuring circuit, which promotes the development of integrated neural networks based on implemented elements.

The magnetoresistance of a planar microbridge based on a three layer Pd0.99Fe0.01–Nb–Pd0.99Fe0.01 FSF sandwich near its superconducting transition is studied. We previously showed that the magnetoresistance curve of such samples is hysteretic and contains dips (negative peaks of the resistance) in the coercive fields. In this work, it is found that the low-resistance state has a memory effect. Functioning of such a sample as a superconducting memory element is demonstrated. The effect of the ferromagnetic Pd0.99Fe0.01 layer on the superconducting transition temperature of the proposed memory element is studied by measuring the dep-endence of the critical temperature of bilayer Pd0.99Fe0.01–Nb FS structures on the thickness of the Pd0.99Fe0.01 layer.

We explore the concept of the Josephson magnetic memory element based on a multilayer two-barrier SIsFS Josephson junction storing the digital state by means of the orientation of magnetization in the F-layer. A diluted PdFe alloy with 1% magnetic atoms is used as a ferromagnet (F), and a tunnel AlOx layer (I) ensures a high voltage in the resistive state. We have studied two junctions of a rectangular shape in which two digital states are defined by the orientation of the residual F-layer magnetization set along or across the junction in the plane of the ferromagnetic barrier. Implementations of both binary and ternary logic elements are demonstrated. A scalability of rectangular memory elements is analyzed using micro-magnetic modeling.

A negative magnetoresistive effect has been observed for ferromagnet/superconductor/ferromagnet (FSF) microbridges based on diluted ferromagnetic PdFe alloy containing as small as 1% of magnetic atoms. The effect is represented by sharp negative peaks in magnetoresistance at magnetic fields opposite in sign to the initial saturated magnetizations. Microstructuring of the FSF trilayers does not suppress the effect: the most pronounced dips were obtained for the smallest bridges 6–8 µm wide and 10–15 µm long. The negative magnetoresistance peak was observed at temperatures within the superconducting transition and reaches a noticeable value of up to 1.3% of the normal state resistance.

A noticeable magnetoresistive effect has been observed on ferromagnet/superconductor/ferromagnet (FSF) microbridges based on diluted ferromagnetic PdFe alloy containing as small as 1% magnetic atoms. Microstructuring of the FSF trilayers does not destroy the effect: the most pronounced curves were obtained on the smallest bridges of 6-8 um wide and 10-15 um long. Below the superconducting transition we are able to control the critical current of microbridges by switching between P and AP orientations of magnetizations of PdFe layers. The operation of FSF-bridge as a magnetic switch is demonstrated in several regimes providing significant voltage discrimination between digital states or remarkably low bit error rate.

We test the effect of an external RF field on the switching processes of magnetic Josephson junctions (MJJs) suitable for the realization of fast, scalable cryogenic memories compatible with Single Flux Quantum logic. We show that the combined application of microwaves and magnetic field pulses can improve the performances of the device, increasing the separation between the critical current levels corresponding to logical “0” and “1.” The enhancement of the current level separation can be as high as 80% using an optimal set of parameters. We demonstrate that external RF fields can be used as an additional tool to manipulate the memory states, and we expect that this approach may lead to the development of new methods of selecting MJJs and manipulating their states in memory arrays for various applications.