Review of the AlGaN/GaN High-Electron-Mobility Transistor-Based Biosensors: Structure, Mechanisms, and Applications.

Due to its excellent material performance, the AlGaN/GaN high-electron-mobility transistor (HEMT) provides a wide platform for biosensing. The high density and mobility of two-dimensional electron gas (2DEG) at the AlGaN/GaN interface induced by the polarization effect and the short distance between the 2DEG channel and the surface can improve the sensitivity of the biosensors. The high thermal and chemical stability can also benefit HEMT-based biosensors' operation under, for example, high temperatures and chemically harsh environments.

Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital.

In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS).

Exploring the Potential of GaN-Based Power HEMTs with Coherent Channel.

The GaN industry always demands further improvement in the power transport capability of GaN-based high-energy mobility transistors (HEMT). This paper presents a novel enhancement-type GaN HEMT with high power transmission capability, which utilizes a coherent channel that can form a three-dimensional electron sea. The proposed device is investigated using the Silvaco simulation tool, which has been calibrated against experimental data.

Latest Advancements in Next-Generation Semiconductors: Materials and Devices for Wide Bandgap and 2D Semiconductors.

Semiconductor materials, devices, and systems have become indispensable pillars supporting the modern world, deeply ingrained in various facets of our daily lives [...

A FIN-LDMOS with Bulk Electron Accumulation Effect.

A thin Silicon-On-Insulator (SOI) LDMOS with ultralow Specific On-Resistance () is proposed, and the physical mechanism is investigated by Sentaurus. It features a FIN gate and an extended superjunction trench gate to obtain a Bulk Electron Accumulation (BEA) effect. The BEA consists of two p-regions and two integrated back-to-back diodes, then the gate potential is extended through the whole p-region.

Jellybean Quantum Dots in Silicon for Qubit Coupling and On-Chip Quantum Chemistry.

The small size and excellent integrability of silicon metal-oxide-semiconductor (SiMOS) quantum dot spin qubits make them an attractive system for mass-manufacturable, scaled-up quantum processors. Furthermore, classical control electronics can be integrated on-chip, in-between the qubits, if an architecture with sparse arrays of qubits is chosen. In such an architecture qubits are either transported across the chip via shuttling or coupled via mediating quantum systems over short-to-intermediate distances.

High-κ perovskite membranes as insulators for two-dimensional transistors.

The scaling of silicon metal-oxide-semiconductor field-effect transistors has followed Moore's law for decades, but the physical thinning of silicon at sub-ten-nanometre technology nodes introduces issues such as leakage currents. Two-dimensional (2D) layered semiconductors, with an atomic thickness that allows superior gate-field penetration, are of interest as channel materials for future transistors. However, the integration of high-dielectric-constant (κ) materials with 2D materials, while scaling their capacitance equivalent thickness (CET), has proved challenging.

Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.

Ethnopharmacological Relevance: The novel coronavirus disease (COVID-19) outbreak in Wuhan has imposed a huge influence in terms of public health and economy on society. However, no effective drugs or vaccines have been developed so far. Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment of this disease due to its clinically proven performance in many severe diseases, like severe acute respiratory syndrome (SARS).

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example.

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not.

On the Baliga's Figure-Of-Merits (BFOM) Enhancement of a Novel GaN Nano-Pillar Vertical Field Effect Transistor (FET) with 2DEG Channel and Patterned Substrate.

A novel enhancement-mode vertical GaN field effect transistor (FET) with 2DEG for reducing the on-state resistance (R) and substrate pattern (SP) for enhancing the breakdown voltage (BV) is proposed in this work. By deliberately designing the width and height of the SP, the high concentrated electric field (E-field) under p-GaN cap could be separated without dramatically impacting the R, turning out an enhanced Baliga's Figure-Of-Merits (BFOM, BV/R). Verified by experimentally calibrated ATLAS simulation, the proposed device with a 700-nm-long and 4.