Introduction of ADC

Chung Chih Hung*, Shih Hsing Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


The detection of biomedical signals needs to be converted into digital signals by an analog-to-digital converter, and then relevant signal processing is performed to identify the characteristics of the biomedical signal so as to become meaningful information for subsequent processing. This chapter introduces the basic working principles of several commonly used analog-to-digital converters (ADC) and simulation details based on digital signal processing. Because different applications have different requirements for ADC speed, accuracy, and power consumption, a suitable ADC architecture needs to be selected for a specific application. We also present the basic operating steps and principles of ADCs, as well as some of its important static and dynamic performance indicators. In addition, the limitations imposed by the working environment will be discussed, including timing jitter and thermal noise limitations. This chapter also introduces common ADC architectures, including FLASH, pipeline, successive approximation, and Sigma-Delta data converters. More detailed discussion of the successive approximation ADC algorithm, calibration technology, and latest development trend are illustrated. In addition, ADC simulation and testing are discussed. Because ADC performance needs to be obtained through digital signal processing approaches, it is necessary to understand some basic knowledge of sampling frequency and input frequency selection, as well as FFT leakage problems. Finally, the commonly used test methods of Nyquist ADCs are presented.

Original languageEnglish
Title of host publicationAnalog Circuits and Signal Processing
Number of pages55
StatePublished - 2022

Publication series

NameAnalog Circuits and Signal Processing
ISSN (Print)1872-082X
ISSN (Electronic)2197-1854


  • Analog-to-Digital Converter (ADC)
  • Auto zero
  • Differential nonlinearity (DNL)
  • Eiffel tower effect
  • Error correcting
  • FFT leakage
  • Flash ADC
  • Hann
  • Integral nonlinearity (INL)
  • Linear ramp histogram
  • Low-leakage bootstrap switch (LRBS)
  • Noise transfer function (NTF)
  • Nyquist
  • Offset error
  • Quantization
  • R-2R
  • Sampling
  • Signal-to-noise ratio (SNR)
  • Sparkle-Tolerant Edge Detector
  • Windowing


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