A promising route to superconducting artificial neural networks is a hybrid digital-analog architecture that combines digital single-flux-quantum (SFQ) communication with compact analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-analog-to-digital (DAD) converter, in which the role of the analog cell was performed by a $\mathrm{\Sigma }$-neuron with a nonlinear transfer function—the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an analog-to-digital converter (ADC), and reencodes the analog $\mathrm{\Sigma }$-neuron wave forms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $\mathrm{\Sigma }$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterized a redesigned $\mathrm{\Sigma }$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $\mathrm{\Sigma }$-neuron transfer characteristic, which reached classification accuracies of $97.0\%$ and $91.9\%$, respectively, on the MNIST handwritten digit dataset.

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• Artificial neural networks
• Josephson effect
• Neuromorphic computing
• Superconductivity