Physics And Computers

Physics for Computer Science Students

presentation of the classical and quantum free electron theories and their successes and shortcomings (Chapter 23). In order to explain the large differences in the electrical properties of solids as well as the peculiar properties of semiconductors, the existence of allowed and forbidden energy bands is investigated (Chapter 24). In this chapter, we introduce the concepts of the electron effective mass and of holes. Intrinsic and doped semiconductors, their electron and hole densities, and their electrical properties are discussed in Chapter 25. It is now a rather simple matter for the student to understand the behavior and the characteristics of semiconductor devices: diodes, bipolar transistors, field effect transistors, etc. Semiconductor devices are the subject of Chapter 26. The text concludes with two chapters unique to this physics textbook. In Chapter 27, we show how diodes and transistors can be used to construct the logic circuits (gates) that constitute the fundamental building blocks of the computer. Chapter 28 is a layman'S introduction to some of the techniques used in the fabrication of integrated circuits. The laboratory experiments for the first semester are standard in any physics department, and thus we do not feel that is is necessary to include them in this book.

Physics and Computers: Problems, Simulations, and Data Analysis

Feynman And Computation

Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.