Analog VLSI Circuits and Princ

This book presents an integrated circuit design methodology that derives its

computational primitives directly from the physics of the used materials and

the topography of the circuitry. The complexity of the performed computations

does not reveal itself in a simple schematic diagram of the circuitry on the

transistor level, as in standard digital integrated circuits, but rather in the

implicit characteristics of each transistor and other device that is represented

by a single symbol in a circuit diagram. The main advantage of this circuitdesign

approach is the possibility of very efficiently implementing certain

‘natural’ computations that may be cumbersome to implement on a symbolic

level with standard logic circuits. These computations can be implemented

with compact circuits with low power consumption permitting highly-parallel

architectures for collective data processing in real time. The same type of

approach to computation can be observed in biological neural structures, where

the way that processing, communication, and memory have evolved has largely

been determined by the material substrate and structural constraints. The data

processing strategies found in biology are similar to the ones that turn out to

be efficient within our circuit-design paradigm and biology is thus a source of

inspiration for the design of such circuits.

The material substrates that will be considered for the circuits in this

book are provided by standard integrated semiconductor circuit technology and

more specifically, by Complementary Metal Oxide Silicon (CMOS) technology.

The reason for this choice lies in the fact that integrated silicon technology

is by far the most widely used data processing technology and is consequently

commonly available, inexpensive, and well-understood. CMOS technology has

the additional advantages of only moderate complexity, cost-effectiveness, and

low power consumption. Furthermore it provides basic structures suitable for

implementation of short-term and long-term memory, which is particularly important

for adaptive and learning structures as found ubiquitously in biological

systems. Although we will specifically consider CMOS technology as a physical

framework it turns out that various fundamental relationships are quite

similar in other frameworks, such as in bipolar silicon technology, in other

semiconductor technologies and to a certain extent also in biological neural

structures. The latter similarities form the basis of neuromorphic emulation of

biological circuits on an electrical level that led to such structures as silicon

neurons and silicon retinas.