arXiv subjects

Zhenhao Liu

Publications and source records attributed to Zhenhao Liu.

3 recordsLinked to original sources

arXiv2026

A CMOS-compatible, scalable and compact magnetoelectric spin-torque microwave detector

The development of compact and highly sensitive microwave detectors compatible with complementary-metal-oxide-semiconductor (CMOS) processes is an active research area but remains a major challenge in microwave technology. Spin-torque diodes (STDs) are emerging nanoscale spintronic devices capable of surpassing the theoretical thermodynamic sensitivity limits of Schottky diodes. However, their practical use in compact systems is limited by the need of external antennas or probes. Here, we demonstrate a magnetoelectric (ME) spin-torque microwave detector that monolithically integrates an ME antenna with a magnetic tunnel junction (MTJ). The device directly converts wireless electromagnetic signals into a DC output at sub-microwatt power levels, achieving a sensitivity greater than 90 kV/W, a noise equivalent power of 3 pW*Hz^-0.5, and a compact footprint of 0.4 mm^2. This performance is due to the nonlinear coupling between incoherent magnetization dynamics, driven by a DC current in the MTJ, and the combined effects of the microwave voltage and strain generated by the ME antenna under incident electromagnetic waves. We further show that this design is scalable, enabling the co-integration of an ME antenna with an array of MTJs. A detector incorporating four MTJs, for example, exhibits a sensitivity exceeding 400 kV/W. This work paves the way for a new generation of highly sensitive, compact and scalable microwave detectors that combine ME antennas and spintronic diodes.

cond-mat.mes-hall↗

arXiv2024

FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objective

Federated Learning (FL) is a distributed machine learning framework to alleviate the data silos, where decentralized clients collaboratively learn a global model without sharing their private data. However, the clients' Non-Independent and Identically Distributed (Non-IID) data negatively affect the trained model, and clients with different numbers of local updates may cause significant gaps to the local gradients in each communication round. In this paper, we propose a Federated Vectorized Averaging (FedVeca) method to address the above problem on Non-IID data. Specifically, we set a novel objective for the global model which is related to the local gradients. The local gradient is defined as a bi-directional vector with step size and direction, where the step size is the number of local updates and the direction is divided into positive and negative according to our definition. In FedVeca, the direction is influenced by the step size, thus we average the bi-directional vectors to reduce the effect of different step sizes. Then, we theoretically analyze the relationship between the step sizes and the global objective, and obtain upper bounds on the step sizes per communication round. Based on the upper bounds, we design an algorithm for the server and the client to adaptively adjusts the step sizes that make the objective close to the optimum. Finally, we conduct experiments on different datasets, models and scenarios by building a prototype system, and the experimental results demonstrate the effectiveness and efficiency of the FedVeca method.

cs.LG↗

arXiv2020

ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications

The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of \textit{vision-aided wireless communications}, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.

cs.LG↗