In clear, easy-to-grasp language, the author covers many of the topics that you will need to know in order to win your dream job and be the first in line for a promotion. In clear, easy-to-grasp language, the author covers many of the topics that you will need to know in order to win your dream job and be the first in line for a promotion.
The operation of semiconductor devices depends upon the use of electrical potential barriers (such as gate depletion) in controlling the carrier densities (electrons and holes) and their transport. Although a successful device design is quite complicated and involves many aspects, the device engineering is mostly to devise a "best" device design by defIning optimal device structures and manipulating impurity profIles to obtain optimal control of the carrier flow through the device. This becomes increasingly diffIcult as the device scale becomes smaller and smaller. Since the introduction of integrated circuits, the number of individual transistors on a single chip has doubled approximately every three years. As the number of devices has grown, the critical dimension of the smallest feature, such as a gate length (which is related to the transport length defIning the channel), has consequently declined. The reduction of this design rule proceeds approximately by a factor of 1. 4 each generation, which means we will be using 0. 1-0. 15 ). lm rules for the 4 Gb chips a decade from now. If we continue this extrapolation, current technology will require 30 nm design rules, and a cell 3 2 size < 10 nm , for a 1Tb memory chip by the year 2020. New problems keep hindering the high-performance requirement. Well-known, but older, problems include hot carrier effects, short-channel effects, etc. A potential problem, which illustrates the need for quantum transport, is caused by impurity fluctuations.
Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed integer optimization problems dynamically, is readily extendable to other industrial processes.
The main topics of this book are:
. hybrid predictive control (HPC) design based on evolutionary multiobjective optimization (EMO);
. HPC based on EMO for dial-a-ride systems; and
. HPC based on EMO for operational decisions in public transport systems.
Hybrid Predictive Control for Dynamic Transport Problems is a comprehensive analysis of HPC and its application to dynamic transport systems. Introductory material on evolutionary algorithms is presented in summary in an appendix. The text will be of interest to control and transport engineers working on the operational optimization of transport systems and to academic researchers working with hybrid systems. The potential applications of the generic methods presented here to other process fields will make the book of interest to a wider group of researchers, scientists and graduate students working in other control-related disciplines."
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