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. This makes creating biosensors with excellent sensitivity, selectivity, reliability, and repeatability achievable using commercialized semiconductor materials. To synthesize the recent developments and advantages in this research field, we review the various AlGaN/GaN HEMT-based biosensors' structures, operations mechanisms, and applications. This review will help new researchers to learn the basic information about the topic and aid in the development of next-generation of AlGaN/GaN HEMT-based biosensors.

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). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.

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 [...].

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. Numerical simulations prove that the proposed device has a very high on-state current above 3 A/mm, while the breakdown voltage (above 800 V) is not significantly affected. The calculated Johnson's and Baliga's figure-of-merits highlight the promise of using such a coherent channel for enhancing the performance of GaN HEMTs in power electronics applications.

A FIN-LDMOS with Bulk Electron Accumulation Effect.

A thin Silicon-On-Insulator (SOI) LDMOS with ultralow Specific On-Resistance (Ron,sp) 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 VGS is extended through the whole p-region. Additionally, the gate oxide Woxide is inserted between the extended superjunction trench gate and N-drift. In the on-state, the 3D electron channel is produced at the P-well by the FIN gate, and the high-density electron accumulation layer formed in the drift region surface provides an extremely low-resistance current path, which dramatically decreases the Ron,sp and eases the dependence of Ron,sp on the drift doping concentration (Ndrift). In the off-state, the two p-regions and N-drift deplete from each other through the gate oxide Woxide like the conventional SJ. Meanwhile, the Extended Drain (ED) increases the interface charge and reduces the Ron,sp. The 3D simulation results show that the BV and Ron,sp are 314 V and 1.84 mΩ∙cm-2, respectively. Consequently, the FOM is high, reaching up to 53.49 MW/cm2, which breaks through the silicon limit of the RESURF.

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. This paper investigates the charge and spin characteristics of an elongated quantum dot-a so-called jellybean quantum dot-for the prospects of acting as a qubit-qubit coupler. Charge transport, charge sensing, and magneto-spectroscopy measurements are performed on a SiMOS quantum dot device at mK temperature and compared to Hartree-Fock multi-electron simulations. At low electron occupancies where disorder effects and strong electron-electron interaction dominate over the electrostatic confinement potential, the data reveals the formation of three coupled dots, akin to a tunable, artificial molecule. One dot is formed centrally under the gate and two are formed at the edges. At high electron occupancies, these dots merge into one large dot with well-defined spin states, verifying that jellybean dots have the potential to be used as qubit couplers in future quantum computing architectures.

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 currents1. Two-dimensional (2D) layered semiconductors, with an atomic thickness that allows superior gate-field penetration, are of interest as channel materials for future transistors2,3. However, the integration of high-dielectric-constant (κ) materials with 2D materials, while scaling their capacitance equivalent thickness (CET), has proved challenging. Here we explore transferrable ultrahigh-κ single-crystalline perovskite strontium-titanium-oxide membranes as a gate dielectric for 2D field-effect transistors. Our perovskite membranes exhibit a desirable sub-one-nanometre CET with a low leakage current (less than 10-2 amperes per square centimetre at 2.5 megavolts per centimetre). We find that the van der Waals gap between strontium-titanium-oxide dielectrics and 2D semiconductors mitigates the unfavourable fringing-induced barrier-lowering effect resulting from the use of ultrahigh-κ dielectrics4. Typical short-channel transistors made of scalable molybdenum-disulfide films by chemical vapour deposition and strontium-titanium-oxide dielectrics exhibit steep subthreshold swings down to about 70 millivolts per decade and on/off current ratios up to 107, which matches the low-power specifications suggested by the latest International Roadmap for Devices and Systems5.

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). Meanwhile, many reports suggest that the side-effects (SE) of TCM prescriptions cannot be ignored in treating COVID-19 as it often leads to dramatic degradation of the patients' physical condition. Systematic evaluation of TCM regarding its latent SE becomes a burning issue. AIM: In this study, we used an ontology-based side-effect prediction framework (OSPF) developed from our previous work and Artificial Neural Network (ANN)-based deep learning, to evaluate the TCM prescriptions officially recommended by China for the treatment of COVID-19. MATERIALS AND METHODS: The OSPF developed from our previous work was implemented in this study, where an ontology-based model separated all ingredients in a TCM prescription into two categories: hot and cold. A database was created by converting each TCM prescription into a vector which contained ingredient dosages, corresponding hot/cold attribution and safe/unsafe labels. This allowed for training of the ANN model. A safety indicator (SI), as a complement to SE possibility, was then assigned to each TCM prescription. According to the proposed SI, from high to low, the recommended prescription list could be optimized. Furthermore, in interest of expanding the potential treatment options, SIs of other well-known TCM prescriptions, which are not included in the recommended list but are used traditionally to cure flu-like diseases, are also evaluated via this method. RESULTS: Based on SI, QFPD-T, HSBD-F, PMSP, GCT-CJ, SF-ZSY, and HSYF-F were the safest treatments in the recommended list, with SI scores over 0.8. PESP, QYLX-F, JHQG-KL, SFJD-JN, SHL-KFY, PESP1, XBJ-ZSY, HSZF-F, PSSP2, FFTS-W, and NHSQ-W were the prescriptions most likely to be unsafe, with SI scores below 0.1. In the additional lists of other TCM prescriptions, the indicators of XC-T, SQRS-S, CC-J, and XFBD-F were all above 0.8, while QF-Y, XZXS-S, BJ-S, KBD-CJ, and QWJD-T's indicators were all below 0.1. CONCLUSIONS: In total, there were 10 TCM prescriptions with indicators over 0.8, suggesting that they could be considered in treating COVID-19, if suitable. We believe this work could provide reasonable suggestions for choosing proper TCM prescriptions as a supplementary treatment for COVID-19. Furthermore, this work introduces a novel and informative method which could help create recommendation list of TCM prescriptions for the treatment of other diseases.

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. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

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 (RON) 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 RON, turning out an enhanced Baliga's Figure-Of-Merits (BFOM, BV2/RON). Verified by experimentally calibrated ATLAS simulation, the proposed device with a 700-nm-long and 4.6-µm-width SP features six times higher BFOM in comparison to the FET without patterned substrate. Furthermore, the proposed pillar device and the SP inside just occupy a nano-scale area, enabling a high-density integration of such devices, which renders its high potential in future power applications.