Strained Silicon Layer in CMOS Technology

Semiconductor industry is currently facing with the fact that conventional submicron CMOS technology is approaching the end of their capabilities, at least when it comes to scaling the dimensions of the components. Therefore, much attention is paid to device technology that use new technological structures and new channel materials. Modern technological processes, which mainly include ultra high vacuum chemical vapor deposition, molecular beam epitaxy and metal-organic molecular vapor deposition, enable the obtaining of ultrathin, crystallographically almost perfect, strained layers of high purity. In this review paper we analyze the role that such layers have in modern CMOS technologies. It’s given an overview of the characteristics of both strain techniques, global and local, with special emphasis on performance of NMOS biaxial strain and PMOS uniaxial strain. Due to the improved transport properties of strained layers, especially high mobility of charge carriers, the emphasis is on mechanisms to increase the charge mobility of strained silicon and germanium, in light of recent developments in CMOS technology

Power electronics: converters and regulators

This book is the result of the extensive experience the authors gained through their year-long occupation at the Faculty of Electrical Engineering at the University of Banja Luka. Starting at the fundamental basics of electrical engineering, the book guides the reader into this field and covers all the relevant types of converters and regulators. Understanding is enhanced by the given examples, exercises and solutions. Thus this book can be used as a textbook for students, for self-study or as a reference book for professionals

Deep learning-based algorithm for mobile robot control in textureless environment

For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%

Deep learning-based algorithm for mobile robot control in textureless environment

For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%