Engineering Materials Science
1995 aza. · Elsevier
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Liburu elektroniko honi buruz
Milton Ohring's Engineering Materials Science integrates the scientific nature and modern applications of all classes of engineering materials. This comprehensive, introductory textbook will provide undergraduate engineering students with the fundamental background needed to understand the science of structure-property relationships, as well as address the engineering concerns of materials selection in design, processing materials into useful products, andhow material degrade and fail in service. Specific topics include: physical and electronic structure; thermodynamics and kinetics; processing; mechanical, electrical, magnetic, and optical properties; degradation; and failure and reliability. The book offers superior coverage of electrical, optical, and magnetic materials than competing text.The author has taught introductory courses in material science and engineering both in academia and industry (AT&T Bell Laboratories) and has also written the well-received book, The Material Science of Thin Films (Academic Press).Key Features* Provides a modern treatment of materials exposing the interrelated themes of structure, properties, processing, and performance* Includes an interactive, computationally oriented, computer disk containing nine modules dealing with structure, phase diagrams, diffusion, and mechanical and electronic properties* Fundamentals are stressed* Of particular interest to students, researchers, and professionals in the field of electronic engineering
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Dr. Milton Ohring, author of two previously acclaimed Academic Press books,The Materials Science of Thin Films (l992) and Engineering Materials Science (1995), has taught courses on reliability and failure in electronics at Bell Laboratories (AT&T and Lucent Technologies). From this perspective and the well-written tutorial style of the book, the reader will gain a deeper physical understanding of failure mechanisms in electronic materials and devices; acquire skills in the mathematical handling of reliability data; and better appreciate future technology trends and the reliability issues they raise.
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