arXiv subjects

Arash Fereidouni

Publications and source records attributed to Arash Fereidouni.

6 recordsLinked to original sources

arXiv2026

Frequency-modulated enhancement of microwave resonator sensing

We use the Pound-Drever-Hall (PDH) technique to characterize the frequency stability of a microwave-frequency surface acoustic wave (SAW) resonator-based sensor. The multi-mode acoustic resonator is integrated in a notch geometry with a transmission line, all fabricated on Y-cut lithium niobate. We measure the amplitude and phase of the resonator transfer function and the PDH signal across the resonator full spectral range. We use these measurements to emphasize the differences between the PDH measurement and a standard Phase-Locked Loop (PLL) technique. As compared to a PLL, we demonstrate that PDH is insensitive to phase error and exhibits a reduced Allan deviation of the center frequency measurement, in each case by up to an order of magnitude. The method rejects spurious effects and background frequency drift, demonstrating the enhancements possible with PDH-based measurements, which can be realized in a wide range of microwave-frequency resonator-based sensors and devices.

physics.app-ph↗

arXiv2023

Coupling Between Magnetic and Transport Properties in Magnetic Layered Material Mn2-xZnxSb

We synthesized single crystals for Mn2-xZnxSb and studied their magnetic and electronic transport properties. This material system displays rich magnetic phase tunable with temperature and Zn composition. In addition, two groups of distinct magnetic and electronic properties, separated by a critical Zn composition of x = 0.6, are discovered. The Zn-less samples are metallic and characterized by a resistivity jump at the magnetic ordering temperature, while the Zn-rich samples lose metallicity and show a metal-to-insulator transition-like feature tunable by magnetic field. Our findings establish Mn2-xZnxSb as a promising material platform that offers opportunities to study how the coupling of spin, charge, and lattice degrees of freedom governs interesting transport properties in 2D magnets, which is currently a topic of broad interest.

cond-mat.mtrl-sci↗

arXiv2021

Giant topological Hall effect in centrosymmetric tetragonal Mn2-xZnxSb

Topological magnetism typically appears in noncentrosymmetric compounds or compounds with geometric frustration. Here we report the effective tuning of magnetism in centrosymmetric tetragonal Mn2-xZnxSb by Zn substitution. The magnetism is found to be closely coupled to the transport properties, giving rise to to a very large topological hall effect is possibly associated with topological magnetism. Our finding suggests Mn2-xZnxSb is a candidate material for the centrosymmetric tetragonal topological magnetic system, offers opportunities for studying and tuning spin textures and developing near room temperature spin-based devices.

cond-mat.mtrl-sci↗

arXiv2020

Evidence for a magnetic-field induced ideal type-II Weyl state in antiferromagnetic topological insulator Mn(Bi1-xSbx)2Te4

The discovery of Weyl semimetals (WSMs) has fueled tremendous interest in condensed matter physics. WSMs require breaking of either inversion symmetry (IS) or time-reversal symmetry (TRS); they can be categorized into type-I and type-II WSMs, characterized by un-tilted and strongly tilted Weyl cones respectively. Type-I WSMs with breaking of IS or TRS and type-II WSMs with IS breaking have been realized experimentally, but TRS-breaking type-II WSM still remains elusive. In this article, we report an ideal TRS-breaking type-II WSM with only one pair of Weyl nodes observed in the antiferromagnetic topological insulator Mn(Bi1-xSbx)2Te4 under magnetic fields. This state is manifested by a large intrinsic anomalous Hall effect, a non-trivial $\mathrm{π}$ Berry phase of the cyclotron orbit and a large positive magnetoresistance in the ferromagnetic phase at an optimal sample composition. Our results establish a promising platform for exploring the physics underlying the long-sought, ideal TRS breaking type-II WSM.

cond-mat.mtrl-sci↗

arXiv2020

Modulation Doping via a 2d Atomic Crystalline Acceptor

Two-dimensional (2d) nano-electronics, plasmonics, and emergent phases require clean and local charge control, calling for layered, crystalline acceptors or donors. Our Raman, photovoltage, and electrical conductance measurements combined with \textit{ab initio} calculations establish the large work function and narrow bands of $α$-RuCl$_3$ enable modulation doping of exfoliated, chemical vapor deposition (CVD), and molecular beam epitaxy (MBE) materials. Short-ranged lateral doping (${\leq}65\ \text{nm}$) and high homogeneity are achieved in proximate materials with a single layer of \arucl. This leads to the highest monolayer graphene (mlg) mobilities ($4,900\ \text{cm}^2/ \text{Vs}$) at these high hole densities ($3\times10^{13}\ \text{cm}^{-2}$); and yields larger charge transfer to bilayer graphene (blg) ($6\times10^{13}\ \text{cm}^{-2}$). We further demonstrate proof of principle optical sensing, control via twist angle, and charge transfer through hexagonal boron nitride (hBN).

cond-mat.mtrl-sci↗

arXiv2020

Defining Quantum Neural Networks via Quantum Time Evolution

This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. Quantum Neural Networks (QNNs) have been explored for over 20 years. The current forefront work based on Variational Quantum Circuits is specifically defined for the Continuous Variable (CV) Model of quantum computers. In this work, a model is proposed which is defined at a more fundamental level and hence can be inherited by any variants of quantum computing models. This work also presents a quantum backpropagation algorithm to train our QNN model and validate this algorithm on the MNIST dataset on a quantum computer simulation.

cs.LG↗