Capabilities of the 3D-MLSI software tool in superconducting neuron design

• Irina E. Tarasova,
• Nikita S. Shuravin,
• Liubov N. Karelina,
• Fedor A. Razorenov,
• Evgeny N. Zhardetsky,
• Aleksandr S. Ionin,
• Mikhail M. Khapaev and
• Vitaly V. Bol’ginov

Beilstein J. Nanotechnol. 2026, 17, 122–138, doi:10.3762/bjnano.17.8

• design of superconducting electronics components. Keywords: adiabatic superconductor cells; inductance extraction; Josephson interferometers; multilayer niobium technology; superconductivity; Introduction This article is devoted to one of the issues related to the design of adiabatic superconducting

• based on Josephson interferometers, the energy potential of which strongly depends on the inductance of the loop. For that reason, the extraction of inductances of superconducting structures has been attracting a lot of attention for many decades. Simple estimates can be made for a long line over

• aimed at the enhancement of accuracy and performance. An agreement as good as ±6% of the experimental values was demonstrated for the set of two-junction Josephson interferometers, including partial loops of superconducting sigma and Gauss neurons. The experimental TF of a sigma neuron was successfully

Transfer function of an asymmetric superconducting Gauss neuron

• Fedor A. Razorenov,
• Aleksander S. Ionin,
• Nikita S. Shuravin,
• Liubov N. Karelina,
• Mikhail S. Sidel’nikov,
• Sergey V. Egorov and
• Vitaly V. Bol’ginov

Beilstein J. Nanotechnol. 2025, 16, 1160–1170, doi:10.3762/bjnano.16.85

• a previously conducted experiment. Keywords: Josephson interferometers; superconducting neural network; superconductivity; Introduction Over the past decade, artificial neural networks have demonstrated their effectiveness and versatility in tasks related to processing large volumes of data