Rapid cryogenic characterisation of 1024 integrated silicon quantum dots

• URL: http://arxiv.org/abs/2310.20434v1
• Date: Tue, 31 Oct 2023 13:14:43 GMT
• Title: Rapid cryogenic characterisation of 1024 integrated silicon quantum dots
• Authors: Edward J. Thomas, Virginia N. Ciriano-Tejel, David F. Wise, Domenic Prete, Mathieu de Kruijf, David J. Ibberson, Grayson M. Noah, Alberto Gomez-Saiz, M. Fernando Gonzalez-Zalba, Mark A. I. Johnson, John J. L. Morton
• Abstract summary: We demonstrate the integration of 1024 silicon quantum dots with on-chip digital and analogue electronics, all operating below 1 K. Key quantum dot parameters are extracted by fast automated machine learning routines to assess quantum dot yield and understand the impact of device design. Results show how rapid large-scale studies of silicon quantum devices can be performed at lower temperatures and measurement rates orders of magnitude faster than current probing techniques.
• Score: 0.6819010383838326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computers are nearing the thousand qubit mark, with the current focus on scaling to improve computational performance. As quantum processors grow in complexity, new challenges arise such as the management of device variability and the interface with supporting electronics. Spin qubits in silicon quantum dots are poised to address these challenges with their proven control fidelities and potential for compatibility with large-scale integration. Here, we demonstrate the integration of 1024 silicon quantum dots with on-chip digital and analogue electronics, all operating below 1 K. A high-frequency analogue multiplexer provides fast access to all devices with minimal electrical connections, enabling characteristic data across the quantum dot array to be acquired in just 5 minutes. We achieve this by leveraging radio-frequency reflectometry with state-of-the-art signal integrity, reaching a minimum integration time of 160 ps. Key quantum dot parameters are extracted by fast automated machine learning routines to assess quantum dot yield and understand the impact of device design. We find correlations between quantum dot parameters and room temperature transistor behaviour that may be used as a proxy for in-line process monitoring. Our results show how rapid large-scale studies of silicon quantum devices can be performed at lower temperatures and measurement rates orders of magnitude faster than current probing techniques, and form a platform for the future on-chip addressing of large scale qubit arrays.

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