You're viewing papers too quickly. Please wait a moment.<br>This helps keep the archive available for everyone.
Quick Navigation
Topics
Trapped Ion Quantum Computing
Superconducting Qubits
Quantum Machine Learning
Hidden Structural Variants in ALD NbN Superconducting Trilayers Revealed by Atomistic Analysis
arXiv
Authors: Prachi Garg, Danqing Wang, Hong X. Tang, Baishakhi Mazumder
Year
2025
Paper ID
16052
Status
Preprint
Abstract Read
~2 min
Abstract Words
209
Citations
N/A
Abstract
Microscopic inhomogeneity within superconducting films is a critical bottleneck hindering the performance and scalability of quantum circuits. All-nitride Josephson Junctions (JJs) have attracted substantial attention for their potential to provide enhanced coherence times and enable higher temperature operation. However, their performance is often limited by local variations caused by polymorphism, impurities, and interface quality. This work diagnoses atomic-scale limitations preventing superconducting NbN/AlN/NbN JJs from reaching their full potential. Electrical measurements reveal suppressed critical current density and soft onset of quasiparticle current. However, inverse proportionality between resistance and junction area confirms homogenous barrier thickness. This isolates structural and chemical variations in electrodes and barrier as the source of performance limitation. The observed characteristics are attributed to complex materials problems: NbN polymorphism, phase coexistence, and oxygen impurities. Using advanced microscopy and machine learning integrated approach, nanoscale inclusions of epsilon-Nb2N2 are found to coexist within dominant delta-NbN electrodes. DC performance of JJs may be affected by these defects, leading to unresolved supercurrent and soft transition to normal state. By identifying specific atomic scale defects, tracing its origin to initial film nucleation, and linking to its detrimental electrical signature, this work establishes a material-to-device correlation and provides targeted strategy for phase engineering towards reproducible, high coherence and scalable quantum devices.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- Microscopic inhomogeneity within superconducting films is a critical bottleneck hindering the performance and scalability of quantum circuits.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.