Sensing supercurrents using geometric effects
Adam N. McCaughan, Nathnael Abebe, Qing-Yuan Zhao, Karl K. Berggren

TL;DR
This paper introduces a superconducting three-terminal device that uses geometric current crowding effects to sense supercurrents without disrupting the superconducting state, enabling non-invasive current measurement.
Contribution
The paper presents a novel Y-shaped superconducting device leveraging geometric effects for non-invasive supercurrent sensing, inspired by fluid flow systems.
Findings
The device can accurately sense supercurrents via critical current modulation.
Current crowding at the intersection enables local interaction without breaking superconductivity.
Potential for broad application in superconducting technologies.
Abstract
We describe a superconducting three-terminal device that uses a simple geometric effect known as current crowding to sense the flow of current and actuate a readout signal. The device consists of a "Y"-shaped current combiner, with two currents (sense and bias) entering through the top arms of the "Y", intersecting, and then exiting through the bottom leg of the "Y"'. This geometry--mixing two inputs at a sharp intersection point--takes its inspiration from Y-shaped combiners in fluid flow systems, where variations in the input pressures can produce at turbulence and mixing at the intersection. When current is added to or removed from one of the arms (the sense arm), the superconducting critical current in the other arm (the bias arm) is modulated. The current in the sense arm can thus be determined by measuring the critical current of the bias arm. The dependence of the bias critical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlack Holes and Theoretical Physics · Physics of Superconductivity and Magnetism · Computational Physics and Python Applications
