Effect of High Temperature on the Performance of AlGaN/GaN T-Gate High-Electron Mobility Transistors with ~140-Nm Gate Length
IEEE TRANSACTIONS ON ELECTRON DEVICES(2024)
Air Force Res Lab
Abstract
High temperature (HT) electronics applica-tions will require the development of a broad range ofdevices made using different materials. Among thesedevices, high-electron mobility transistors (HEMTs) madewith GaN and its alloys are attractive for high-power radiofrequency (RF) applications. In this manuscript, we testedAlGaN/GaN HEMT devices having similar to 140-nm gate length atdifferent temperatures up to 500 degrees C. Devices were fab-ricated using Air Force Research Laboratory's (AFRL's)140-nmT-gate process technology. The performancedegradation measured in different devices was analyzed byconsidering changes in different device parameters and byusing appropriate device physics. Cross-sectional materi-als characterization using scanning transmission electronmicroscopy (STEM) and electron energy loss spectroscopy(EELS) was performed to understand the origin of perfor-mance degradation. This understanding will allow us todesign a sub-mu m GaN-based process technology compat-ible with HT RF applications
MoreTranslated text
Key words
Logic gates,Temperature measurement,MODFETs,HEMTs,Performance evaluation,Metals,Wide band gap semiconductors,AlGaN/GaN high electron mobility transistors (HEMTs),degradation,high temperature (HT) electronics,leakage,time-dependence,transconductance
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined