Modeling and Simulation in Engineering

The general aim of this book is to present selected chapters of the following types: chapters with more focus on modeling with some necessary simulation details and chapters with less focus on modeling but with more simulation details. This book contains eleven chapters divided into two sections: Modeling in Continuum Mechanics and Modeling in Electronics and Engineering. We hope our book entitled "Modeling and Simulation in Engineering - Selected Problems" will serve as a useful reference to students, scientists, and engineers

Nano-scale TG-FinFET: Simulation and Analysis

Transistor has been designed and fabricated in the same way since its invention more than four decades ago enabling exponential shrinking in the channel length. However, hitting fundamental limits imposed the need for introducing disruptive technology to take over. FinFET - 3-D transistor - has been emerged as the first successor to MOSFET to continue the technology scaling roadmap. In this thesis, scaling of nano-meter FinFET has been investigated on both the device and circuit levels. The studies, primarily, consider FinFET in its tri-gate (TG) structure. On the device level, first, the main TCAD models used in simulating electron transport are benchmarked against the most accurate results on the semi-classical level using Monte Carlo techniques. Different models and modifications are investigated in a trial to extend one of the conventional models to the nano-scale simulations. Second, a numerical study for scaling TG-FinFET according to the most recent International Technology Roadmap of Semiconductors is carried out by means of quantum corrected 3-D Monte Carlo simulations in the ballistic and quasi-ballistic regimes, to assess its ultimate performance and scaling behavior for the next generations. Ballisticity ratio (BR) is extracted and discussed over different channel lengths. The electron velocity along the channel is analyzed showing the physical significance of the off-equilibrium transport with scaling the channel length. On the circuit level, first, the impact of FinFET scaling on basic circuit blocks is investigated based on the PTM models. 256-bit (6T) SRAM is evaluated for channel lengths of 20nm down to 7nm showing the scaling trends of basic performance metrics. In addition, the impact of VT variations on the delay, power, and stability is reported considering die-to-die variations. Second, we move to another peer-technology which is 28nm FD-SOI as a comparative study, keeping the SRAM cell as the test block, more advanced study is carried out considering the cell‘s stability and the evolution from dynamic to static metrics

SYNTHESIS AND EVALUATION OF ANTIMICROBIAL ACTIVITY OF PHENYL AND FURAN-2-YL[1,2,4] TRIAZOLO[4,3-a]QUINOXALIN-4(5H)-ONE AND THEIR HYDRAZONE PRECURSORS

A variety of 1-(s-phenyl)-[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one (3a-3h) and 1-(s-furan-2-yl)-[1,2,4]triazolo[4,3- a]quinoxalin-4(5H)-one (5a-d) were synthesized from thermal annelation of corresponding hydrazones (2a-h) and (4a-d) respectively in the presence of ethylene glycol which is a high boiling solvent. The structures of the compounds prepared were confirmed by analytical and spectral data. Also, the newly synthesized compounds were evaluated for possible antimicrobial activity. 3-(2-(4-hydroxylbenzylidene)hydrazinyl)quinoxalin-2(1H)-one (2e) was the most active antibacterial agent while 1-(5-Chlorofuran-2-yl)-[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one (5c) stood out as the most potent antifungal agent

Advances in Solid State Circuit Technologies

This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields

GPU-based implementation of real-time system for spiking neural networks

Real-time simulations of biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and robotics, prosthetics, neurological and neuroscientific modeling. BNNs possess inherently parallel architecture and operate in continuous signal domain. Spiking neural networks (SNNs) are type of BNNs with reduced signal dynamic range: communication between neurons occurs by means of time-stamped events (spikes). SNNs allow reduction of algorithmic complexity and communication data size at a price of little loss in accuracy. Simulation of SNNs using traditional sequential computer architectures results in significant time penalty. This penalty prohibits application of SNNs in real-time systems. Graphical processing units (GPUs) are cost effective devices specifically designed to exploit parallel shared memory-based floating point operations applied not only to computer graphics, but also to scientific computations. This makes them an attractive solution for SNN simulation compared to that of FPGA, ASIC and cluster message passing computing systems. Successful implementations of GPU-based SNN simulations have been already reported. The contribution of this thesis is the development of a scalable GPU-based realtime system that provides initial framework for design and application of SNNs in various domains. The system delivers an interface that establishes communication with neurons in the network as well as visualizes the outcome produced by the network. Accuracy of the simulation is emphasized due to its importance in the systems that exploit spike time dependent plasticity, classical conditioning and learning. As a result, a small network of 3840 Izhikevich neurons implemented as a hybrid system with Parker-Sochacki numerical integration method achieves real time operation on GTX260 device. An application case study of the system modeling receptor layer of retina is reviewed

ELECTRON DEVICE NONLINEAR MODELLING FOR MICROWAVE CIRCUIT DESIGN

The electron device modelling is a research topic of great relevance, since the performances required to devices are continuously increasing in terms of frequency, power and linearity: new technologies are affirming themselves, bringing new challenges for the modelling community. In addition, the use of monolithic microwave integrated circuits (MMIC) is also increasing, making necessary the availability, in the circuit design phase, of models which are computationally efficient and at the same more and more accurate. The importance of modelling is even more evident by thinking at the wide area covered by microwave systems: terrestrial broadband, satellite communications, automotive applications, but also military industry, emergency prevention systems or medical instrumentations. This work contains a review of the empirical modelling approach, providing the description of some well-known equivalent-circuit and black-box models. In addition, an original modelling approach is described in details, together with the various possible applications: modelling of nonquasi-static phenomena as well as of low-frequency dispersive effects. A wide experimental validation is provided, for GaAs- and GaN-based devices. Other modelling issues are faced up, like the extraction of accurate models for Cold-FET or the more convenient choice of the data-interpolator in table-based models. Finally, the device degradation is also treated: a new measurement setup will be presented, aimed to the characterization of the device breakdown walkout under actual operating conditions for power amplifiers