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Yonghong Zhao

Publications and source records attributed to Yonghong Zhao.

3 recordsLinked to original sources

Cold source field-effect transistor with type-III band-aligned HfS$_2$/WTe$_2$ heterostructure

The cold source field-effect transistor (CSFET) is promising for reducing power dissipation in integrated circuits by engineering the density of states at the injecting source. Existing CSFET designs utilizing Dirac-source metals or p-Metal-n stacks are challenged by Schottky barriers at the metal-semiconductor interface. In this work, a 2D WTe$_2$/HfS$_2$ heterojunction with type-III band alignment is proposed to be an excellent design of cold source and CSFET. The architecture has a high band-to-band transport mechanism by removing the detrimental Schottky barrier issues. Importantly, the proposed CSFET has the same channel barrier modulation principle as conventional MOSFET to enable a high on-state current. Using first-principles-based quantum transport modeling, we predict a very high $I_{\rm on}$/$I_{\rm off}$ ratio at $\sim$ 10$^{10}$, a low subthreshold swing below the thermal limit for a wide range of gate voltages, reaching as small as 41.3 mV/dec, at low source-drain bias $V_{DS}=0.3$ $\rm V$. These findings establish a design principles for next-generation low-power nanoelectronic switches leveraging 2D van der Waals heterostructures.

cond-mat.mtrl-sci

Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist 2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of 0.894 (hold-out test) and 0.850 (external test). The screening performance of the AI system was better than that of the cardiologists (AI system (0.846) vs. cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI system for CA detection and screening. The system has the potential to be used by healthcare professionals, caregivers, and general users to assess CAs based on real-world ECG images.

cs.CV

Electric control of spin in monolayer WSe2 field effect transistors

We report a first principles theoretical investigation of quantum transport in monolayer WSe2 field effect transistor (FET). Due to a strong spin-orbit interaction (SOI) and the atomic structure of the two-dimensional (2D) lattice, monolayer WSe2 has an interesting electronic structure that exhibits Zeeman-like up-down spin texture near the K and K' points of the Brillouin zone. In a FET, the gate electric field induces an extra, externally tunable SOI that re-orients the spins into a Rashba-like texture thereby realizing electric control of the spin. Quantum transport is modulated by the spin texture, namely by if the spin orientation of the carrier after the gated channel region, matches or miss-matches that of the FET drain electrode. The carrier current in the FET is labelled both the spin index and the valley index, realizing spintronics and valleytronics in the same device.

cond-mat.mes-hall