An efficient approach to engineering the Al ₂ O ₃ /GaN positive interface fixed charges by post-dielectric annealing in nitrogen is demonstrated. The remarkable reduction of interface fixed charges from 1.44 × 10 ¹³ to 3 × 10 ¹² cm ^-2 was observed, which leads to a record high threshold voltage (V _TH ) of 7.6 V obtained in the Al ₂ O ₃ /GaN MOSFETs. The significantly reduced interface fixed charges and the corresponding remote scattering effect enable respectable improvement in the electron mobility that results in a high drain current density of 355 mA/mm in the device. These competitive results reveal that the method reported in this letter is promising in pushing V _TH more positive and simultaneously achieving good device performance of normally-off GaN power devices.

A small amount of methoxide additive is found to effectively induce preferential adsorption of anions ( i.e. , TFSI ⁻ and NO ₃ ⁻ ) on the surface of the Li metal anode, contributing to an inorganic-rich SEI and enabling stable Li metal batteries.

The semiconductors industry benefits greatly from the integration of machine learning (ML)-based techniques in technology computer-aided design (TCAD) methods. The performance of ML models, however, relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of experimental data points and do not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network (DNN)-based prediction task for the ohmic resistance value in gallium nitride (GaN) devices. A 70% reduction in mean absolute error (MAE) when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.

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.