By Marcel Pelgrom
This textbook is suitable to be used in graduate-level curricula in analog to electronic conversion, in addition to for practising engineers short of a cutting-edge reference on information converters. It discusses quite a few analog-to-digital conversion rules, together with sampling, quantization, reference new release, nyquist architectures and sigma-delta modulation. This publication provides an outline of the cutting-edge during this box and makes a speciality of problems with optimizing accuracy and velocity, whereas decreasing the facility point.
This new, moment variation emphasizes novel calibration strategies, the categorical requisites of latest platforms, the results of 22-nm know-how and the necessity for a extra statistical method of accuracy. Pedagogical improvements to this variation comprise greater than two times the workouts on hand within the first version, solved examples to introduce all key, new options and warnings, feedback and tricks, from a practitioner’s standpoint, anywhere acceptable. substantial history info and functional counsel, from designing a PCB, to lay-out points, to trade-offs on process point, supplement the dialogue of simple rules, making this e-book a priceless reference for the skilled engineer.
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Extra info for Analog-to-Digital Conversion
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32) where K1/ f is the characteristic coefficient. Svv is the power noise density and has as dimension Volt2 /Hz. 1). In order to estimate the impact of the noise on a system the above power density noise term must be integrated over the relevant frequency span. An intriguing question is on the apparent explosion of “1/ f ” to infinity at zero frequency. Some authors consider the utilization of the electronic equipment as a lower frequency limit. As an example for the lower limit, the daily rhythm can be chosen with a frequency f = 10−5 Hz.
The most common form of noise is thermal noise due to the Brownian motion of the charge carriers in a conductor. Thermal noise in a frequency band d f is described by the thermal noise density Svv : thermal noise density in 1 Hz:Svv ( f ) = 4kT R √ √ or:Sv = 4kT R [V Hz] f = f high rms noise voltagevnoise,rms = 4kT Rd f f = f low = 4kT R( fhigh − flow ) = √ 4kT RBW [V], where k is Boltzmann’s constant, T the absolute temperature, and BW the bandwidth of interest. Thermal noise is assumed to have a “white noise” or flat frequency distribution: its magnitude is constant for all frequencies.
9 [8, p. 405]. 9 Fourier series expansions of signals repeating at a period T [6–8] Square wave (transition at t = 0) −1 − T /2 < t < 0 f (t) = +1 0 < t < T /2 Square ⎧ wave symmetrical around t = 0 −1 − T /2 < t < −T /4 ⎪ ⎪ ⎨ f (t) = +1 − T /4 < t < T /4 ⎪ ⎪ ⎩ −1 T /4 < t < T /2 Square ⎧ wave with Tc high period −1 − T /2 < t < −Tc /2 ⎪ ⎪ ⎨ f (t) = +1 − Tc /2 < t < Tc /2 ⎪ ⎪ ⎩ −1 Tc /2 < t < T /2 Triangle⎧ 2t ⎪ ⎪ − T /2 < t < 0 ⎨− T f (t) = ⎪ ⎪ ⎩ + 2t 0 < t < T /2 T Saw tooth t f (t) = 0