Evaluation of GaN HEMT Technology Development Through Nonlinear Characterization

Author(s):  
A. Angelini ◽  
V. Camarchia ◽  
F. Cappelluti ◽  
S.D. Guerrieri ◽  
M. Pirola ◽  
...  
2010 ◽  
Vol 31 (11) ◽  
pp. 114004 ◽  
Author(s):  
Chi Chen ◽  
Yue Hao ◽  
Ling Yang ◽  
Si Quan ◽  
Xiaohua Ma ◽  
...  

2021 ◽  
Author(s):  
Rakesh Kaneriya ◽  
Gunjan Rastogi ◽  
Palash Basu ◽  
Rajesh Upadhyay ◽  
Apurba Bhattacharya

Terahertz (THz) technology has attracted tremendous attention recently due to its promising applications in various domains such as medical, biological, industrial imaging, broadband, safety, communication, radar, space science, and so on. Due to non-availability of powerful sources and highly sensitive and efficient detectors, the so-called THz gap remains largely unfilled. Despite seamless efforts from electronics and photonics technology researchers, the desired level of technology development to fill the THz gap still remains a challenge. GaN-based HEMT structures have been investigated as potential THz sources and detectors by a number of researchers. This chapter presents a very new and versatile mechanism for electrical tuning of intersubband transitions (ISBT) GaN high electron mobility transition (HEMT) devices. ISBT phenomena are usually demonstrated in photonic devices like a quantum cascade laser (QCL). Here we explore ISBT in an electronic GaN HEMT device. Conventional photonic devices like a QCL are operated at cryogenic temperature to minimize thermal effect. Tuning the conduction band through external gate bias is an advantage of an HEMT device for room temperature (RT) THz applications. This chapter demonstrates the theoretical and experimental novel ISBT phenomenon in GaN HEMT is for potential ambient applications in the THz range.


Author(s):  
Simon Thomas

Trends in the technology development of very large scale integrated circuits (VLSI) have been in the direction of higher density of components with smaller dimensions. The scaling down of device dimensions has been not only laterally but also in depth. Such efforts in miniaturization bring with them new developments in materials and processing. Successful implementation of these efforts is, to a large extent, dependent on the proper understanding of the material properties, process technologies and reliability issues, through adequate analytical studies. The analytical instrumentation technology has, fortunately, kept pace with the basic requirements of devices with lateral dimensions in the micron/ submicron range and depths of the order of nonometers. Often, newer analytical techniques have emerged or the more conventional techniques have been adapted to meet the more stringent requirements. As such, a variety of analytical techniques are available today to aid an analyst in the efforts of VLSI process evaluation. Generally such analytical efforts are divided into the characterization of materials, evaluation of processing steps and the analysis of failures.


2012 ◽  
pp. 117-131 ◽  
Author(s):  
O. Golichenko

The problems of multifold increase of technological potential of developing countries are considered in the article. To solve them, i.e. to organize effectively tapping into global knowledge and their absorption, the performance of two diffusion channels is considered: open knowledge transfer and commercial knowledge transfer. The models of technological catching-up are investigated. Two of them are found to give an opportunity of effective use of international competition and global technology knowledge as a driver of technology development.


Author(s):  
M. Jeyanthi ◽  
C. Velayutham

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.


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