The age of electronics began in the early 1900s with Pickard's creation of the crystal diode detector, Fleming's invention of the diode vacuum tube, and then Deforest's development of the triode vacuum tube. Since that time, the electronics industry has grown to account for as much as 10 percent of the world gross domestic product.
The real catalysts for the explosive growth of electronics occurred following World War II. The first was the invention of the bipolar transistor by Bardeen, Brattain, and Shockley in1947; the second was the simultaneous invention of the integrated circuit by Kilby and by Noyce and Moore in 1958.
Integrated circuits quickly became a commercial reality, and the complexity, whether measured in memory density (bits/chip), microprocessor transistor count, or minimum feature size, has changed exponentially since the mid-1960s. We are now in an era of ultra-large-scale integration (ULSI), having already put lower levels of integration--SSI, MSI, LSI,and VLSI--behind us.
Electronic circuit design deals with two major categories of signals. Analog electrical signals may take on any value within some finite range of voltage or current. Digital signals, however, can take on only a finite set of discrete levels. The most common digital signals are binary signals, which are represented by two discrete levels.
Bridging between the analog and digital worlds are the digital-to-analog and analog-to-digital conversion circuits (DAC and ADC, respectively). The DAC converts digital information into an analog voltage or current, whereas the ADC creates a digital number at its output that is proportional to an analog input voltage or current.
Fourier demonstrated that complex signals can be represented as a linear combination of sinusoidal signals. Analog signal processing is applied to these signals using linear amplifiers; these modify the amplitude and phase of analog signals. Linear amplifiers do not alter the frequency content of the signal, other than changing the relative amplitudes and phases of the frequency components.
Amplifiers are often classified by their frequency response into low-pass, high-pass, band-pass, band-reject, and all-pass categories. Electronic circuits that are designed to amplify specific ranges of signal frequencies are usually referred to as filters.
Solving problems is one focal point of an engineer's career. A well-defined approach can help significantly in solving problems, and to this end, a structured problem-solving approach has been introduced in this chapter as outlined in these nine steps. Throughout the rest of this text, the examples will follow this problem-solving approach:
State the problem as clearly as possible.
List the known information and given data.
Define the unknowns that must be found to solve the problem.
List your assumptions. You may discover additional assumptions as the analysis progresses.
Develop an approach from a list of possible alternatives.
Perform an analysis to find a solution to the problem.
Check the results. Is the math correct? Have all the unknowns been found? Do the results satisfy simple consistency checks?
Evaluate the solution. Is the solution realistic? Can it be built? If not, repeat steps 4-7 until a satisfactory solution is obtained.
Use computer-aided analysis to check the results and to see if the solution satisfies the problem requirements.
Our circuit designs will be implemented using real components whose initial values differ from those of the design and that change with time and temperature. Techniques for analyzing the influence of element tolerances on circuit performance include the worst-case analysis and statistical Monte Carlo analysis methods. Most circuit analysis programs include the ability to specify temperature dependencies for most circuit elements.
In worst-case analysis, element values are simultaneously pushed to their extremes, and the resulting predictions of circuit behavior are often overly pessimistic.
The Monte Carlo method analyzes a large number of randomly selected versions of a circuit to build up a realistic estimate of the statistical distribution of circuit performance. Random number generators in high-level computer languages, spreadsheets, Mathcad®,or MATLAB® can be used to randomly select element values for use in Monte Carlo analysis. Some circuit analysis packages such as PSPICE provide a Monte Carlo analysis option as part of the program.
