arXiv subjects

E. S. Joseph

Publications and source records attributed to E. S. Joseph.

7 recordsLinked to original sources

arXiv2026

Depth-Resolved Lattice Distortions in a Silicon-Germanium Qubit Host

Semiconductor qubits, promising for quantum computation, inherit properties from their host lattice. Quantum dot spins, occupying the local lowest energy states in the conduction band, necessarily couple to structural disorder and interfaces. While silicon-based systems promise low noise alongside industrially compatible manufacturing, the growth of SiGe---a leading platform---unavoidably introduces lattice dislocations, inhomogeneous strain, and crosshatch patterns, expected to cause fluctuations between devices, qubit failure, and subsequently higher operational overhead. Through X-ray nano-structural mapping of an Intel Si/SiGe chip, we reveal, with 30$~$nm lateral and 200$~$nm functional depth resolution, how extended lattice defects introduced during growth propagate through the heterostructure, creating permanently distorted lattice planes and strain. We correlate these at the $\approx1~μ$m scale of a quantum dot device and calculate the impact on qubit energy spectra. We observe crosshatch fine structure and find that substrate miscut and growth correlate with the final crosshatch pattern.

cond-mat.mes-hall↗

arXiv2026

Direct measurement of the energy spectrum of a quantum dot qubit

The mapping between gate voltages applied to a double quantum dot, and the parameters of a Hubbard-like Hamiltonian, is of utmost importance for understanding and operating spin qubits. State-of-the-art techniques for measuring Hamiltonian parameters (e.g., detuning axis pulsed spectroscopy, DAPS) provide details about energy levels; however, tunnel coupling estimates typically reveal only a small portion of the full Hamiltonian. Here, we demonstrate a Hamiltonian-agnostic technique for measuring the double dot energy spectrum over a wide energy range, at every value of the detuning, called delta-axis spectroscopy (DAXS). We apply the DAXS method to obtain the energy spectrum of a Si/SiGe double quantum dot and use this data to extract the diagonal and off-diagonal couplings of a 15-level Hubbard-like Hamiltonian, demonstrating very good agreement with the experimental measurements.

quant-ph↗

arXiv2025

A scanning probe microscopy approach for identifying defects in aluminum oxide

The coherence of quantum dot qubits fabricated in semiconductors is often limited by charge noise from defects in gate dielectrics, which are material- and process-dependent. Characterizing these defects is an important step towards reducing their impact and improving qubit coherence. The identification of individual defects requires atomic-scale spatial resolution, however, and sufficient spectral sensitivity to determine their electronic structure. Electrostatic force microscopy (EFM) provides highly resolved maps of the surface potential of dielectrics, and importantly, is also sensitive to single-electron charging processes that reflect the spectral structure of underlying defects. In this work, we use cryogenic EFM to characterize aluminum oxide grown by atomic layer deposition (ALD) on bulk silicon. These measurements reveal defects close to the surface that exchange electrons with the EFM tip as they transition through different charge states. Detailed electrostatic modeling opens the door to powerful techniques for mapping tip-backgate charging voltages onto defect transition energies, allowing defects such as aluminum vacancies, and carbon, oxygen, or hydrogen impurities to be identified, by comparing to density functional theory (DFT). These results point towards EFM as a powerful tool for exploring defect structures in solid-state qubits.

cond-mat.mes-hall↗

arXiv2025

Reducing strain fluctuations in quantum dot devices by gate-layer stacking

Nanofabricated metal gate electrodes are commonly used to confine and control electrons in electrostatically defined quantum dots. However, these same gates impart strain-induced potential fluctuations that can potentially impair device functionality. Here we investigate strain fluctuations in Si/SiGe heterostructures, caused by (i) lattice mismatch, (ii) materials-dependent thermal contraction, and (iii) depositional stress in the metal gates. By simulating gate geometries, ranging from simple to realistically complicated, we identify two opposing effects in overlapping gate structures: (a) gate-driven behavior arising from isolated gates vs (b) oxide-driven behavior arising from the thin oxides separating the gates in an overlapping geometry. These limiting behaviors induce strains of opposite sign, pointing towards the possibility of suppressing strain fluctuations through careful design. Here, we demonstrate nearly total suppression of short-range strain fluctuation through device optimization. These results suggest that strain fluctuations should not pose an insurmountable challenge to qubit uniformity, provided that oxide and overlapping gate thicknesses can be tuned.

cond-mat.mes-hall↗

arXiv2025

Non-equilibrium Dynamics of Two-level Systems directly after Cryogenic Alternating Bias

Two-level systems (TLSs) are tunneling states commonly found in amorphous materials that electrically couple to qubits, resonators, and vibrational modes in materials, leading to energy loss in those systems. Recent studies suggest that applying a large alternating electric field changes the oxide structure, potentially improving the performance of qubits and resonators. In this study, we probe the effect of alternating bias at cryogenic temperatures on TLS dynamics within amorphous oxide parallel-plate capacitors operating in the strongly coupled regime. We bias the TLSs in the capacitors using an electric field. This allows us to spectroscopically image TLSs and extract their densities and dipole moments. When an in-situ alternating bias is applied, the steady-state spectra from the standard TLS model disappear. Post-alternating bias TLS spectroscopy reveals transient behavior, in which the TLS frequency fluctuates on the order of minutes. Thermal cycling above 10 K reverses these effects, restoring the TLS spectrum to its original state, indicating a reversible mechanism. Importantly, the intrinsic loss tangent of the LC oscillator remains unchanged before and after the application of the alternating bias. We propose that the disappearance of the steady-state spectrum are caused by non-equilibrium energy build up from strain in the oxide film introduced by the pulsed voltage bias sequence. Understanding this non-equilibrium energy could inform future models of time-dependent TLS dynamics.

quant-ph↗

arXiv2024

Coupling conduction-band valleys in SiGe heterostructures via shear strain and Ge concentration oscillations

Engineering conduction-band valley couplings is a key challenge for Si-based spin qubits. Recent work has shown that the most reliable method for enhancing valley couplings entails adding Ge concentration oscillations to the quantum well. However, ultrashort oscillation periods are difficult to grow, while long oscillation periods do not provide useful improvements. Here, we show that the main benefits of short-wavelength oscillations can be achieved in long-wavelength structures through a second-order coupling process involving Brillouin-zone folding induced by shear strain. We finally show that such strain can be achieved through common fabrication techniques, making this an exceptionally promising system for scalable quantum computing.

cond-mat.mes-hall↗

arXiv2022

Toward Robust Autotuning of Noisy Quantum Dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a "gatekeeper" system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) % when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

quant-ph↗