Chapter 1. Electronics and Semiconductors Tong In Oh 1
Objective Understanding electrical signals Thevenin and Norton representations of signal sources Representation of a signal as the sum of sine waves Analog and digital representations of a signal Signal amplification (amplifier) 2
Introduction Microelectronics Based on the integrated-circuit (IC) technology Contain billions of components in a small piece of silicon Equal to a complete digital computer (microcomputer, microprocessor) Shall study Electronic devices in the discrete circuits / components of an integrated circuits Design and analysis of interconnections of these devices Available IC chips and their applications In this Chapter 1 Basic concepts and terminology Signal/signal-processing(signal amplification) Circuit representations or models for linear amplifiers Design and analysis of electronic circuits Properties and physics of semiconductors Monolithic circuit/crystal structure of semiconductors/electron/hole/doping/carrier drift/carrier diffusion/pn junction 3
1.1 Signals Contain information (patient monitor, voice of a radio announcer, etc) To extract required information from a set of signals Need to process the signals in predetermined manner Signal processing performed by electronic systems Transducer: convert the various forms of physical signals to electrical signals Two types of signal sources Thevenin form / Norton form v s t = R s i s (t) Figure 1.1 Two alternative representations of a signal source: (a) the Thévenin form; (b) the Norton form. 4
Time-varying quantity Represented by the changes in its magnitude as time progresses Difficult to characterize of describe succinctly for signal-processing 5
1.2 Frequency Spectrum of Signals Extremely useful characterization of a signal Obtained through the mathematical tools Fourier series/fourier transform: representing a voltage or current signal as the sum of sine-wave signals of different frequencies and amplitudes v a t = V a sin ωt + θ V a : peak value, amplitude ω: angular frequency (radians/second) ω = 2πf rad/s f: frequency in hertz f = 1/T Hz T: period in seconds θ: phase Sine-wave is characterized by its peak value(v a )/frequency(f)/phase(θ) Magnitude of signal: root-mean-square (rms) value (V a / 2) 6
Fourier series: Representation of signals as the sum of sinusoids In the special case for a periodic function of time Fourier transform: Obtain the frequency spectrum of a signal In general case for an arbitrary function of time Ex) expressed a given periodic function of time as the sum of an infinite number of harmonic sinusoids v t = 4V π (sin ω 0t + 1 3 sin 3ω 0t + 1 5 sin 5ω 0t + ) V: amplitude of the square wave ω 0 : fundamental frequency Figure 1.5 A symmetrical square-wave signal of amplitude V. 7 Figure 1.6 The frequency spectrum (also known as the line spectrum) of the periodic square wave of Fig. 1.5.
Matlab Code for Fourier Series 8
For a nonperiodic function of time, Frequency spectrum as a continuous function of frequency Signal representation Time-domain representation (v a t ): waveform varies with time Frequency-domain representation (V a ω ): frequency spectrum Figure 1.7 The frequency spectrum of an arbitrary waveform such as that in Fig. 1.3. 9
Matlab Code for Fourier Transform 10
Homework Example 1.1 Summary of Thevenin and Norton s theorem Summary of Fourier series/fourier transform Recommendation Running Matlab code 11