Spectroscopy With Intelligent Feature Tracking for automated characterization of excited-state structure in semiconductor quantum dot devices
Abstract
The characterization of excited-state structure in semiconductor quantum dot (QD) devices is an important component of tuning them for spin-qubit operation. We present Spectroscopy With Intelligent Feature Tracking (SWIFT), a framework that combines machine-learning (ML)-assisted feature identification with physics-informed geometric processing to extract energy-level splittings from pulsed-gate spectroscopy data. SWIFT isolates the relevant spectral features and exploits their characteristic geometry to reduce the two-dimensional spectroscopy analysis to a one-dimensional peak-detection problem. It further combines an ensemble-based confidence metric with sequential accumulation of rapid, low-SNR scans, allowing the inferred spectrum to be reevaluated as experimental evidence accumulates. Using Si/SiGe QD devices, we demonstrate SWIFT both offline and in real time, including automated tracking of QD excited states and lead resonances. Benchmarking on 255 manually labeled scans shows that SWIFT reduces the median splitting error to 0.05 mV from 0.11 mV for a classical baseline, with the largest improvement on lower-quality measurements. These results provide a path toward incorporating excited-state spectroscopy into autonomous QD characterization, tuning, and optimization, which will be essential in large-scale quantum dot devices.
Related
- Nonlinear Photocurrent Spectroscopy and Polarization-Tunable Shift Current in the Layered Semiconductor CuScP2S6
- Quantum digital simulations of time-domain semiconductor spectroscopy in the semi-classical approximation
- Towards Intelligent Educational Assessment: A Multimodal NLP Framework for Automated Feedback Generation and Personalized Learning Analytics
- Decoupling Trap States in Amorphous Oxide Semiconductor Transistor Using a Conductive-Semiconducting-Conductive MOS Structure
- Automated end-station at the soft X-ray beamline featuring robotic air-to-ultrahigh-vacuum sample transfer, scanning probe spectroscopy, and machine learning analysis
BibTeX
@misc{huffman2026spectroscopy,
title = {{Spectroscopy With Intelligent Feature Tracking for automated characterization of excited-state structure in semiconductor quantum dot devices}},
author = {A. R. Huffman and Daniel Schug and Merritt Losert and Sanghyeok Park and Daniel King and Yuna Chun and Giordano Scappucci and M. A. Eriksson and Justyna P. Zwolak},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05686},
eprint = {2609.05686},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05686},
}
RIS
TY - UNPB
TI - Spectroscopy With Intelligent Feature Tracking for automated characterization of excited-state structure in semiconductor quantum dot devices
AU - A. R. Huffman
AU - Daniel Schug
AU - Merritt Losert
AU - Sanghyeok Park
AU - Daniel King
AU - Yuna Chun
AU - Giordano Scappucci
AU - M. A. Eriksson
AU - Justyna P. Zwolak
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.05686
UR - https://arxiv.org/abs/2609.05686
AB - The characterization of excited-state structure in semiconductor quantum dot (QD) devices is an important component of tuning them for spin-qubit operation. We present Spectroscopy With Intelligent Feature Tracking (SWIFT), a framework that combines machine-learning (ML)-assisted feature identification with physics-informed geometric processing to extract energy-level splittings from pulsed-gate spectroscopy data. SWIFT isolates the relevant spectral features and exploits their characteristic geometry to reduce the two-dimensional spectroscopy analysis to a one-dimensional peak-detection problem. It further combines an ensemble-based confidence metric with sequential accumulation of rapid, low-SNR scans, allowing the inferred spectrum to be reevaluated as experimental evidence accumulates. Using Si/SiGe QD devices, we demonstrate SWIFT both offline and in real time, including automated tracking of QD excited states and lead resonances. Benchmarking on 255 manually labeled scans shows that SWIFT reduces the median splitting error to 0.05 mV from 0.11 mV for a classical baseline, with the largest improvement on lower-quality measurements. These results provide a path toward incorporating excited-state spectroscopy into autonomous QD characterization, tuning, and optimization, which will be essential in large-scale quantum dot devices.
ER -