Bootstrapping, autonomous testing, and initialization system for Si/SixGe1-x multi-quantum-dot devices
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Abstract (arXiv)
Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from trapped charges within the oxide layers, which induce random offset voltage shifts on gate electrodes, with a standard deviation of approximately 83 mV of variation within state-of-the-art present-day devices. Efficient characterization and tuning of large arrays of QD qubits depend on choices of automated protocols. Here, we introduce a physically intuitive framework for a bootstrapping, autonomous testing, and initialization system (BATIS) designed to streamline QD device evaluation and calibration. BATIS navigates high-dimensional gate voltage spaces, automating essential steps such as leakage testing, formation of all current channels, and gate characterization in the presence of trapped charges. For forming the current channels, BATIS follows a non-standard approach that requires a single set of measurements regardless of the number of channels. Demonstrated at 1.3 K on a quad-QD Si/SixGe1-x device, BATIS eliminates the need for deep cryogenic environments during initial device diagnostics, significantly enhancing scalability and reducing setup times. By requiring only minimal prior knowledge of the device architecture, BATIS represents a platform-agnostic solution, adaptable to various QD systems, which bridges a critical gap in QD autotuning.
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References · 9
- 2021Colloquium: Advances in automation of quantum dot devices controlcited by 18
- 2021Ray-based framework for state identification in quantum dot devicescited by 17
- 2022Tuning arrays with rays: Physics-informed tuning of quantum dot charge statescited by 16
- 2021Toward Robust Autotuning of Noisy Quantum Dot Devicescited by 13
- 2023Automated extraction of capacitive coupling for quantum dot systemscited by 11
- 2021Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learningcited by 7
- 2023Data needs and challenges for quantum dot devices automationcited by 6
- 2024Autonomous Bootstrapping of Quantum Dot Devicescited by 4
- 2024Automation of Quantum Dot Measurement Analysis via Explainable Machine Learningcited by 2
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