Predictive Simulation of Semiconductor Processing enables researchers and developers to extend the scaling range of semiconductor devices beyond the parameter range of empirical research. It requires a thorough understanding of the basic mechanisms employed in device fabrication, such as diffusion, ion implantation, epitaxy, defect formation and annealing, and contamination. This book presents an in-depth discussion of our current understanding of key processes and identifies areas that require further work in order to achieve the goal of a comprehensive, predictive process simulation tool.

This book contains a comprehensive review of CMP (Chemical-Mechanical Planarization) technology, one of the most exciting areas in the field of semiconductor technology. It contains detailed discussions of all aspects of the technology, for both dielectrics and metals. The state of polishing models and their relation to experimental results are covered. Polishing tools and consumables are also covered. The leading edge issues of damascene and new dielectrics as well as slurryless technology are discussed.

Organic semiconductors offer unique characteristics which have prompted the application of organic semiconductors and their devices in physical, chemical, and biological sensors. This book covers this emerging field by discussing both optically- and electrically-based sensor concepts. Novel transducers based on organic light-emitting diodes and organic thin-film transistors, as well as systems-on-a-chip architectures are presented. Functionalization techniques are also outlined.

This book reviews the current status of research and development in dilute III-V nitrides. It covers major developments in this new class of materials within 24 chapters from prominent research groups. The book integrates materials science and applications in optics and electronics in a unique way. It is valuable both as a reference work for researchers and as a study text for graduate students.

The idea of theoretically predicting the useful properties of various materials using multiscale simulations has become popular in recent years. Of special interest are nanostructured, organic functional materials, which have a hierarchical structure and are considered materials of the future because of their flexibility and versatility. Their functional properties are inherited from the molecule that lies at the heart of the hierarchical structure. On the other hand, the properties of this functional molecule, in particular its absorption and emission spectra, strongly depend on its interactions with its molecular environment. Therefore, the multiscale simulations used to predict the properties of organic functional materials should be atomistic, that is, they should be based on classical and/or quantum methods that explicitly take into account the molecular structure and intermolecular interactions at the atomic level. This book, written by well-known specialists in theoretical chemistry, focuses on the basics of classical mechanics, quantum chemistry methods used for molecular disordered materials, classical methods of molecular simulations of disordered materials, vibronic interactions, and applications (presented as multiscale strategies for atomistic simulations of photonic materials). It has been edited by Professor Mikhail Alfimov, a renowned Russian scientist, a full member of the Russian Academy of Sciences, Russia, and the founder, first director, and now research supervisor of the Photochemistry Center of the Russian Academy of Science, Russia. Professor Alfimov’s main research interests are in the field of photochemistry and photophysics of molecular and supramolecular systems. The book is a great reference for advanced undergraduate- and graduate-level students of nanotechnology and molecular science and researchers in nano- and molecular science, nanotechnology, chemistry, and physical chemistry, especially those with an interest in functional materials.

This review volume is devoted to a discussion of analogies and differences of complex production systems ? natural, as in biological cells, or man-made, as in economic systems or industrial production. Taking this unified look at production is based on two observations: Cells and many biological networks are complex production units that have evolved to solve production problems in a reliable and optimal way in a highly stochastic environment. On the other hand, industrial production is becoming increasingly complex and often hard to predict. As a result, modeling and control of such production networks involve many different spatial and temporal scales and decision policies for many different structures. The common themes of industrial and biological production include evolution and optimization, synchronization and self-organization, robust operation despite high stochasticity, and hierarchical dynamics. The mathematical techniques used come from dynamical systems theory, transport equations, control theory, pattern formation, graph theory, discrete event simulations, stochastic processes, and others. The application areas range from semiconductor production to supply chains, protein networks, slime molds, social networks, and whole economies.