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Configurable superconducting transport through atomic-scale channel editing

Researchers at Zhejiang University have developed a superconducting diode that allows for post-fabrication reconfiguration of electron flow, using lithography to trim channel edges at the nanoscale.

Dr. Ines Havel

Jul 2, 2026 · 1 min read

Control over the direction of electricity without resistance is the fundamental requirement for superconducting electronics. Traditionally, the properties of superconducting diodes—which allow current to flow freely in one direction while meeting resistance in the other—are fixed at the moment of fabrication. Once the physical structure of a Josephson junction or a patterned vortex pinning site is set, the device’s polarity and efficiency remain static.

At Zhejiang University in Hangzhou, a team led by Yanwu Xie has demonstrated a platform that breaks this rigidity. Working with two-dimensional oxide interfaces composed of lanthanum aluminate and potassium tantalate (LAO/KTO), the researchers found they could "edit" the behavior of the superconductor after it was built. The breakthrough originated from an observation of channel edge imperfections; random defects induced during standard photolithography created asymmetric conditions for magnetic flux, leading to unpredictable diode effects.

By employing conductive atomic force microscope (cAFM) lithography, the team successfully straightened these rough boundaries at an atomic scale. This process does more than just clean a sample; it allows for the deterministic shaping of the superconducting channel's geometry. Because the diode effect in these materials is tied to the movement of vortices along the edges, changing the physical boundary changes the rectification efficiency and the polarity of the device itself.

The ability to reversibly modify these characteristics within a single device suggests a path toward adaptive circuit architectures. Rather than building static components, engineers may soon be able to reconfigure superconducting logic gates and memory elements on demand, optimizing performance by mapping the precise relationship between channel geometry and vortex dynamics.