3D tomography of exchange phase in a Si/SiGe quantum dot device

Dylan Albrecht; Sarah Thompson; N. Tobias Jacobson; Ryan Jock

cond-mat.mes-hallMar 17, 2026

3D tomography of exchange phase in a Si/SiGe quantum dot device

Authors: Dylan Albrecht, Sarah Thompson, N. Tobias Jacobson, Ryan Jock

Organizations: Sandia National Laboratories, Albuquerque NM, USA

Abstract

The exchange interaction is a foundational building block for the operation of spin-based quantum processors. Extracting the exchange interaction coefficient $J(\mathbf{V})$ , as a function of gate electrode voltages, is important for understanding disorder, faithfully simulating device performance, and operating spin qubits with high fidelity. Typical coherent measurements of exchange in spin qubit devices yield a modulated cosine of an accumulated phase, which in turn is the time integral of exchange. As such, extracting $J(\mathbf{V})$ from experimental data is difficult due to the ambiguity of inverting a cosine, the sensitivity to noise when unwrapping phase, as well as the problem of inverting the integral. As a step toward obtaining $J(\mathbf{V})$ , we tackle the first two challenges to reveal the accumulated phase, $φ(\mathbf{V})$ . We incorporate techniques from a wide range of fields to robustly extract and model a 3D phase volume for spin qubit devices from a sequence of 2D measurements. In particular, we present a measurement technique to obtain the wrapped phase, as done in phase-shifting digital holography, and utilize the max-flow/min-cut phase unwrapping method (PUMA) to unwrap the phase in 3D voltage space. We show this method is robust to the minimal observed drift in the device, which we confirm by increasing scan resolution. Upon building a model of the extracted phase, we optimize over the model to locate a minimal-gradient $π$ exchange pulse point in voltage space. Our measurement protocol may provide detailed information useful for understanding the origins of device variability governing device yield, enable calibrating device models to specific devices during operation for more sophisticated error attribution, and enable a systematic optimization of qubit control. We anticipate that the methods presented here may be applicable to other qubit platforms.

Figures & tables

Panel of Figure 1 — Figure 1: Device operation. (a) Schematic illustration of a 12-QD Intel Si/SiGe Tunnel Falls device with magnified view of device region used for this experiment. QDs are formed under plunger gates (P1, P2, P3) on the top half of the device, as indicated by green (addressed during state preparation and measurement) and blue (gauge spin) circles, and are separated by barrier gates (B0, B1, B2). (b) Schematic electrostatic potential diagram for the experimental section of the device under idle conditions (grey) and during an exchange pulse (black). (c) Qubit bloch sphere, where $J_{n}$ is the exchange interaction between QD2 and QD3, and $J_{z}$ is the exchange interaction between QD1 and QD2. (d) Example pulse control sequence during a phase-shifted fingerprint measurement.

Panel of Figure 2 — Figure 2: Measurements. In figure (a) we have an example of a phase raster scan at fixed $V_{B2}$ greater than $V_{B2_{\textrm{idle}}}$ . In figure (b) we have an example of a fingerprint scan taken at $0\degree$ rotation in detuning space, c.f. (c). In figure (c) we show the trajectories through detuning space used to collect fingerprints in (d). Each black line indicates the $V_{P2}$ and $V_{P3}$ coordinates along the detuning ( $V_{P2}$ - $V_{P3}$ ) axis of a fingerprint corresponding to that detuning angle. Trajectories are plotted over a phase raster measured with $V_{B2}=V_{B2_{\textrm{idle}}}$ and prolonged wait times to show the stable detuning limits for this charge state, for reference. The red circle indicates a cylindrical core of data removed when model fitting. Figure (d) shows the fingerprint scans as cross-sectional scans in 3D voltage space, for $0\pi$ phase shift.

Panel of Figure 3 — Figure 3: Data processing pipeline for $0\degree$ detuning. The pipeline steps leading up to a mask applied to the wrapped phase. We use the masks on the unwrapped phases to fit our model to the full 3D data.

Panel of Figure 4 — Figure 4: Model. In (a), we show a rendering of the 3D phase spline model. The mesh outline indicates the convex hull of the confident phase region. From the bottom contour to top, the contours represent $0.1$ , $\pi/4$ , $\pi/2$ , and $\pi$ radians. We have placed a small marble at the location of the symmetric operating point for a $\pi$ exchange pulse where phase gradients with respect to P2 and P3 vanish. In (b), we show a volumetric slice comparison of the experimental data and the spline model.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Time-dependence of phase shift operations on the prepared singlet state. Phase shift outcomes are calculated based on the singlet return probability for 11 random idle coordinates, defined by $V_{B2}=$ 0 V and $\lvert V_{P2}-V_{P3}\rvert<$ 14 mV, in each fingerprint measurement. Error bars represent standard deviation. To partially correct for drift in the SET output signal, SET reference measurements are recorded during qubit reset, and the difference in SET output during reset and measurement is pre-calibrated and assumed to be constant. (a) Unreferenced and (b) Referenced measurements of each phase shift outcome, as a function of time since the first measurement in a series for a given phase shift condition. (c) Unreferenced and (d) Referenced measurements of each phase shift outcome as a function of detuning angle condition.

Panel of Figure 6 — Figure 6: High-resolution phase rasters. We show the successful phase unwrapping of high resolution scans, $300\times 300$ pixels. This demonstrates that drift has not corrupted the phase extraction due to drift over $\sim 12$ hrs.

Mar 27, 2026

Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models

Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability diagrams (or CSDs, data maps which crucially define the occupation of QDs) is time-consuming, particularly in emerging architectures where CSDs must be acquired with remote sensors that cannot probe the charge of the relevant dots directly. In this work, we present a generative approach to accelerate acquisition by reconstructing full CSDs from sparse measurements, using a conditional diffusion model. We evaluate our approach using two experimentally motivated masking strategies: uniform grid-based sampling, and line-cut sweeps. Our lightweight architecture, trained on approximately 9,000 examples, successfully reconstructs CSDs, maintaining key physically important features such as charge transition lines, from as little as 4% of the total measured data. We compare the approach to interpolation methods, which fail when the task involves reconstructing large unmeasured regions. Our results demonstrate that generative models can significantly reduce the characterization overhead for quantum devices, and provides a robust path towards an experimental implementation.

Vinicius Hernandes, Joseph Rogers, Rouven Koch +5

QuTech and Kavli Institute of Nanoscience, Delft University of Technology, Delft, the Netherlands

Image Reconstruction

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Jun 9, 2026

Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices

Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage. Limited-angle acquisition introduces missing-wedge artefacts such as elongation and anisotropic resolution, while noisy low-dose data further degrade reconstruction quality and quantitative reliability. Here, we introduce an unsupervised deep learning framework based on Deep Image Prior with total variation regularization (DIP-TV) for limited-angle STEM-EDX tomography. We extend it to a multi-channel formulation (DIPm-TV) that jointly reconstructs multiple elemental maps by exploiting spatial correlations. Using a synthetic 3-channel phantom, we show that the method compensates for severe missing-wedge artefacts corresponding to approximately

100^\circ

of missing angular range under moderate noise, outperforming simultaneous iterative reconstruction technique and compressed sensing approaches. We apply the method to 3D chemical analysis of Ge-Sb-Te (GST) memory devices in virgin (as-fabricated) and SET (crystalline) operational states. Samples were prepared as cross-sectional focused ion beam lamellae and acquired under a limited-angle tilt range from

-40^\circ

+40^\circ

with

5^\circ

steps and a dose of

2.0\times10^5

e^-/Ang^2

. The multi-channel approach enables voxel-by-voxel elemental reconstruction using only EDX signals without external structural priors such as high-angle annular dark-field imaging. The reconstructed volumes show near-isotropic spatial resolution and reveal compositional heterogeneities associated with device operation. This approach enables 3D chemical characterization in experimentally accessible sample geometries where conventional methods fail due to severe angular limitations.

Daniel del Pozo Bueno, Serge Brosset, Theo Monniez +3

1CEA, LETI, Univ. Grenoble Alpes, Grenoble, 38000, France. · 2CEA, Neurospin, Paris-Saclay University, Gif-sur-Yvette, F-91191, France. · 3Inria, MIND, Palaiseau, F-91210, France.

Deep Image Prior Image Reconstruction Computed Tomography Electron Microscopy Scanning Transmission Electron Microscopy

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Apr 15, 2026

Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram

Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pipeline that performs charge auto-tuning by locating transition lines in full charge stability diagrams (CSDs) and returns gate voltage targets for the single charge regime. We assemble and manually annotate a large, heterogeneous dataset of 1015 experimental CSDs measured from silicon QD devices, spanning nine design geometries, multiple wafers, and fabrication runs. A U-Net style convolutional neural network (CNN) with a MobileNetV2 encoder is trained and validated through five-fold group cross validation. Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs. We analyze dominant failure modes and propose targeted mitigations. Finally, wide-range diagram segmentation also naturally enables scalable physic-based feature extraction that can feed back to fabrication and design workflows and outline a roadmap for real-time integration in a cryogenic wafer prober. Overall, our results show that neural network (NN) based wide-diagram segmentation is a practical step toward automated, high-throughput charge tuning for silicon QD qubits.

Peter Samaha, Amine Torki, Ysaline Renaud +4

CEA-Leti, Univ. Grenoble Alpes, F-38000 Grenoble, France

Topic pending

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Abstract

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