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3 - Sensor Design Optimization and Tradeoffs

from Part I - Fundamentals

Published online by Cambridge University Press:  23 December 2021

Marco Tartagni
Marco Tartagni
Affiliation:
University of Bologna
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Summary

This chapter starts by first describing techniques to reduce errors. As far as the random ones are concerned, reduction approaches oriented to increase the signal-to-noise ratio on the spectrum domain and their strict relationship with sample averaging are discussed. Following, strategies for limitation of systematic errors are presented, especially based on the feedback concept. However, since the error reduction techniques allow several degrees of freedom, this chapter discusses the tradeoffs in optimizing sensing systems from the resolution, bandwidth, and power consumption point of view. More specifically, the resolution optimization of the sensing process is treated under the information theory point of view and the approach is extended to acquisition chains to understand the role of single building blocks.

Keywords

acquisition chainsignal-to-noise ratiosignal-to-noise-and distortion ratiocalibrationfeedback sensinginformation theoryantialiasing filteroversamplingfigure of merit
Type
Chapter
Information
Electronic Sensor Design Principles , pp. 135 - 188
Publisher: Cambridge University Press
Print publication year: 2022

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References

Further Reading

Cover, J. A., and Thomas, T. M., Elements of Information Theory. New York: John Wiley & Sons, 1991.Google Scholar
Gregorian, R., and Temes, G. C., Analog MOS Integrated Circuits. New York: John Wiley & Sons, 1986.Google Scholar
Kester, W., Ed., The Data Conversion Handbook. Philadelphia: Elsevier, 2004.Google Scholar
Schreier, R., and Temes, G. C., Understanding Delta-Sigma Data Converters. New York: IEEE Press, 2005.Google Scholar
Stone, J. V., Information Theory: A Tutorial Introduction. Sebtel Press, 2015.Google Scholar
Walden, R. H., Analog-to-digital converter survey and analysis, IEEE J. Sel. Areas Commun., vol. 17, no. 4, pp. 539–550, 1999.Google Scholar
Widrow, B., and Kollar, I., Quantization Noise. Cambridge: Cambridge University Press, 2008.Google Scholar
Zhirnov, V., and Cavin, R. K. III, Microsystems for Bioelectronics. Philadelphia: Elsevier, 2015.Google Scholar

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