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Sampling Theory, a Renaissance pp 485–517Cite as

Birkhäuser

Digital Adaptive Calibration of Data Converters Using Independent Component Analysis

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Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

Abstract

The theory and practice of applying a neural network model and learning algorithm—Independent Component Analysis (ICA)—to the online adaptive calibration of analog-to-digital converters (ADCs) is covered in this chapter. Exploiting the independence between the input signal and an injected pseudorandom bit sequence (PRBS), the technique attempts to blindly separate the two in the digital conversion output, and while doing so, an equivalent model of the ADC non-idealities is identified, resulting in the subsequent linearization of the conversion process. The ICA framework offers new signal-processing insights into the widely used correlation-based error-parameter identification method for the background calibration of multistage ADCs. In addition, it provides a useful technique to minimize the analog overhead associated with the calibration by simultaneously identifying multiple model parameters using a single PRBS, improving the efficiency and potentially the application regime of the online calibration approach for data converters.

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Notes

  1. 1.

    One can imagine this by sliding the threshold at zero to the left or right while confining it in between the two parallel residue segments with a slope of two.

  2. 2.

    As quantization can be understood as division, the 1.5-bit topology is actually a realization of the Sweeny–Robertson–Tocher (SRT) fast division algorithm well known in computer arithmetic.

  3. 3.

    For simplicity and better clarity, we will drop the constant scaling factor V R in all the equations from this point onward.

  4. 4.

    The reported simulation results correspond to a sinusoidal input waveform in this example. The convergence time and learning accuracy do not seem to depend on the input waveform much, as long as it is busy and occupies most of the input range. The readers are referred to [6] for a more detailed discussion on this.

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Authors and Affiliations

  1. Analog and Mixed-Signal Lab, Texas Analog Center of Excellence, University of Texas at Dallas, Richardson, TX, 75080, USA

    Yun Chiu

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  1. Yun Chiu
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Correspondence to Yun Chiu .

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Editors and Affiliations

  1. School of Engineering and Science, Jacobs University Bremen, Bremen, Bremen, Germany

    Götz E. Pfander

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Chiu, Y. (2015). Digital Adaptive Calibration of Data Converters Using Independent Component Analysis. In: Pfander, G. (eds) Sampling Theory, a Renaissance. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-19749-4_14

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