The predictions of Moore’s law are considered by experts to be valid until 2020 giving rise to “post-Moore’s” technologies afterwards. Energy efficiency is one of the major challenges in high-performance computing that should be answered. Superconductor digital technology is a promising post-Moore’s alternative for the development of supercomputers. In this paper, we consider operation principles of an energy-efficient superconductor logic and memory circuits with a short retrospective review of their evolution. We analyze their shortcomings in respect to computer circuits design. Possible ways of further research are outlined.

Motivated by recent progress in the development of cryogenic memory compatible with single flux quantum (SFQ) circuits, we have performed a theoretical study of magnetic SIsFS Josephson junctions, where “S” is a bulk superconductor, “s” is a thin superconducting film, “F” is a metallic ferromagnet, and “I” is an insulator. We calculate the Josephson current as a function of s and F layers thickness, temperature, and exchange energy of F film. We outline several modes of operation of these junctions and demonstrate their unique ability to have large product of a critical current IC and a normal-state resistance RN in the π state, comparable to that in superconductor–insulator–superconductor tunnel junctions commonly used in SFQ circuits. We develop a model describing switching of the Josephson critical current in these devices by external magnetic field. The results are in good agreement with the experimental data for Nb-Al/AlOx-Nb-Pd0.99Fe0.01-Nb junctions.

We consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron (MLP). Their compact circuits contain just one and two Josephson junctions, respectively. While the signal is represented as magnetic flux, the proposed cells are inherently nonlinear and close-to-linear magnetic flux transformers. The neuron is capable of providing the one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features both positive and negative signal transfer coefficients in the range $\sim (-0.5,0.5)$ . We briefly discuss implementation issues and further steps toward the multilayer adiabatic superconducting artificial neural network, which promises to be a compact and the most energy-efficient implementation of MLP.

We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

In this work, we study theoretically the properties of S-F/N-sIS type Josephson junctions in the frame of the quasiclassical Usadel formalism. The structure consists of two superconducting electrodes (S), a tunnel barrier (I), a combined normal metal/ferromagnet (N/F) interlayer, and a thin superconducting film (s). We demonstrate the breakdown of a spatial uniformity of the superconducting order in the s-film and its decomposition into domains with a phase shift π. The effect is sensitive to the thickness of the s layer and the widths of the F and N films in the direction along the sIS interface. We predict the existence of a regime where the structure has two energy minima and can be switched between them by an electric current injected laterally into the structure. The state of the system can be non-destructively read by an electric current flowing across the junction.