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Superconducting Qubits Quantum Machine Learning

Robotic chip-scale nanofabrication for superior consistency

arXiv
Authors: Felix M. Mayor, Wenyan Guan, Erik Szakiel, Amir H. Safavi-Naeini, Samuel Gyger

Year

2025

Paper ID

16719

Status

Preprint

Abstract Read

~2 min

Abstract Words

116

Citations

N/A

Abstract

Unlike the rigid, high-volume automation found in industry, academic research requires process flexibility that has historically relied on variable manual operations. This hinders the fabrication of advanced, complex devices. We propose to address this gap by automating these low-volume, high-stakes tasks using a robotic arm to improve process control and consistency. As a proof of concept, we deploy this system for the resist development of Josephson junction devices. A statistical comparison of the process repeatability shows the robotic process achieves a resistance spread across chips close to 2%, a significant improvement over the 7% spread observed from human operators, validating robotics as a solution to eliminate operator-dependent variability and a path towards industrial-level consistency in a research setting.

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.
  • Unlike the rigid, high-volume automation found in industry, academic research requires process flexibility that has historically relied on variable manual operations.

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