EE210 Digital Electronics Class Lecture 2 September 03, 2008

1. EE210Digital ElectronicsClass Lecture 2September 03, 2008

2. Sedra/SmithMicroelectronic Circuits5/e Oxford University Press

3. Introduction to Electronics 3

4. In This Class We Will Discuss Following Topics : 1.1 Signals Thévenin &Norton Theorem (Append. C) 1.2 Frequency Spectrum of Signals 1.3 Analog and Digital Signals

5. 1.1 Signals • Signals Contain Information • To Extract Information Signals Need to be PROCESSED in Some Predetermined Manner • Electronic System Process Signals Conveniently • Signal Must be an Electric Entity, V or I • Transducers Convert Physical Signal into Electric Signal

6. vs (t) = Rsis(t) Two alternative representations of a signal source: (a) the Thévenin form, and (b) the Norton form.

7. Appendix C Thévenin’s theorem.

8. Norton’s Theorem

9. Thévenin& Norton Points to Note: • Two Representations are Equivalent • Parameters are Related as: vs (t) = Rsis(t) • Thévenin Preferred When RsLow • Norton Preferred When RsHigh

10. Example C.1 Apply Thévenin’s Theorem to Simplify A BJT Circuit

11. An arbitrary voltage signal vs(t). Signal is a Quantity That Varies in Time. Information is Contained in the Change in Magnitude as Time Progresses. Difficult to Characterize Mathematically

12. 1.2 Frequency Spectrum of Signals • Signal (or Any Arb. Function of Time) Characterization in Terms of Frequency Spectrum, using Fourier Series/Transform • FS and FT Help Represent Signal as Sum of Sine-wave Signals of Different Frequencies and Amplitudes • Use FS When Signal is Periodic in Time • Use FT When Signal is Arbitrary in Time

13. Sine-wave voltage signal of amplitude Va and frequency f = 1/T Hz. The angular frequency ω= 2πf rad/s. Continued 

14. Amplitude Va of Sine-wave Signal Commonly Expressed in RMS = Va / √2 • Household 220 V is an RMS Value • FS allows us to Express ANY Periodic Function of Time as Sum of Infinite Number of Sinusoids Whose Frequencies are Harmonically Related, e.g., The Square-wave Signal in Next Slide.

15. Using FS Square-wave Signal can be Expressed as: v(t) = 4V/π (sin ωot + 1/3 sin 3 ωot + 1/5 sin 5 ωot + …) with ωo = 2 π/ T is Fundamental Frequency Sinusoidal Components Makeup Frequency Spectrum

16. The Frequency Spectrum (Also Known As The Line Spectrum) Of The Previous Periodic Square Wave • Note That Amplitude of Harmonics Progressively Decrease • Infinite Series Can be Truncated for Approximation

17. FT can be Applied to Non-Periodic Functions of time, such as: And Provides Frequency Spectrum as a Continuous Function of Frequency, Such As:

18. The Frequency Spectrum of Previous Arbitrary Non-periodic Waveform

19. Periodic Non-Periodic Periodic Signals Consists of Discrete Freq. Non-Periodic Signals Contains ALL Freq. HOWEVER, …

20. The Useful Parts of the Spectra of Practical Signals are Confined to Short Segments of Frequency, e.g., Audio Band is 20 Hz to 20kHz • In Summary, We can Represent A Signal : • In Time-Domain va(t) • In Frequency-Domain Va(ω)

21. 1.3 Analog and Digital Signals • This is an Analog Signal as it is Analogous to Physical Signal it Represents • Its Amplitude Continuously Varies Over Its Range of Activity

22. Digital Signal is Representation of the Analog Signal in Sequence of Numbers • Each Number Representing The Signal Magnitude at An Instant of Time • Let us Take the Analog Signal and Convert it To Digital Signal by SAMPLING • Sampling is a Process of Measuring The Magnitude of a Signal at an Instant of Time

23. Sampling The Continuous-time Analog Signal in (a)Results in The Discrete-time Signal in(b)

24. Original Signal is Now Only Defined at Sampling Instants – No More Continuous, Rather Discrete Time Signal, Still Analog as Mag. Is Cont. • If Magnitude of Each Sample is Represented by Finite Number of Digits Then Signal Amplitude will Also be Quantized, Discretized or Digitized • Then, Signal is Digital --- A Sequence of Numbers That Represent Mag. of Successive Signal Samples

25. The Choice of Number System to Represent Signal Samples Affects the Type of Digital Signal Produced and Also Affects the Complexity of Dig. Circuits • The BINARY Number System Results in Simplest Possible Signals and Circuits • In a Binary Number Digit is Either 0 or 1 • Correspondingly, Two Voltage Levels (Low or High) for Digital Signal • Most Digital Circuits Have 0 V or 5V

26. Time Variation of a Binary Digital Signal • Note That: Waveform is a Pulse Train with 0 V Representing 0 or Logic 0 and 5V Rep. Logic 1

27. Binary Rep. of Analog Signal To use N Binary Digits (bits) to Represent Each Sample of The Analog Signal, the Digitized Sample Value Can be as: D = b0 20 + b1 21 + b2 22 + … + bN-1 2N-1 Where, b0 , b1 ,… bN-1 are N bits with value 0 or 1 b0 is LSB and bN-1 is MSB Binary Number Written as: bN-1 bN-2 … b0

28. The Binary Rep (Cont…) • Quantizes Analog Sample in 2N Levels • Greater the Number of Bits (Larger N) Closer the Digital Word D Approx. to the Magnitude of the Analog Sample • Large N Reduces the Quantization Error and Increases the Resolution of Analog-to-Digital Conversion (Increases Cost as Well)

29. Block-diagram Representation Of The Analog-to-digital Converter (ADC) – A Building Block of Modern Electronic Systems

30. Once Signal is in Digital Form it Can be Processed by Digital Circuits • Digital Circuits also Process Signals which do Not Have Analog Origin, e.g., Signals Representing Digital Computer Instruction • As Digital Circuits Deal With Binary Signals Their Design is Simpler Than of Analog Circuits • While Digital Circuit Design has Its Own Challenges, It Provides Reliable and Economic Implementations of Many Signal Processing Functions not Possible With Analog Circuits

31. In Next Class We Will Continue to Discuss: Chapter 1: Introduction to Electronics Topics: 1.4 Amplifiers 1.7 Logic Inverters 1.8 Circuit Simulation Using SPICE