资 源 简 介
This book presents an integrated circuit design methodology that derives itscomputaTIonal primiTIves directly from the physics of the used materials andthe topography of the circuitry. The complexity of the performed computaTIonsdoes not reveal itself in a simple schemaTIc diagram of the circuitry on thetransistor level, as in standard digital integrated circuits, but rather in theimplicit characteristics of each transistor and other device that is representedby a single symbol in a circuit diagram. The main advantage of this circuitdesignapproach is the possibility of very efficiently implementing certain‘natural’ computations that may be cumbersome to implement on a symboliclevel with standard logic circuits. These computations can be implementedwith compact circuits with low power consumption permitting highly-parallelarchitectures for collective data processing in real time. The same type ofapproach to computation can be observed in biological neural structures, wherethe way that processing, communication, and memory have evolved has largelybeen determined by the material substrate and structural constraints. The dataprocessing strategies found in biology are similar to the ones that turn out tobe efficient within our circuit-design paradigm and biology is thus a source ofinspiration for the design of such circuits.The material substrates that will be considered for the circuits in thisbook are provided by standard integrated semiconductor circuit technology andmore specifically, by Complementary Metal Oxide Silicon (CMOS) technology.The reason for this choice lies in the fact that integrated silicon technologyis by far the most widely used data processing technology and is consequentlycommonly available, inexpensive, and well-understood. CMOS technology hasthe additional advantages of only moderate complexity, cost-effectiveness, andlow power consumption. Furthermore it provides basic structures suitable forimplementation of short-term and long-term memory, which is particularly importantfor adaptive and learning structures as found ubiquitously in biologicalsystems. Although we will specifically consider CMOS technology as a physicalframework it turns out that various fundamental relationships are quitesimilar in other frameworks, such as in bipolar silicon technology, in othersemiconductor technologies and to a certain extent also in biological neuralstructures. The latter similarities form the basis of neuromorphic emulation ofbiological circuits on an electrical level that led to such structures as siliconneurons and silicon retinas.
