Linear Noisy Networks with Stochastic Components

Noyan C. Sevuktekin, Maxim Raginsky, Andrew C. Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution


All circuit components have uncertainties inherent to the underlying fabrication process that, in large networks, may change the overall circuit response unpredictably. In the absence of a robust general model to incorporate individual component uncertainties and the concomitant stochastic thermal noise characteristics, under-simulating from the massively high-dimensional experiment space via Monte Carlo techniques and over-designing the final product against potentially undiscovered faults have become de facto standard. Such practices do not only cost simulation time, circuit area, and power, but also provide a partial understanding of the underlying uncertainty and of ways to exploit it. This paper investigates the impact of component uncertainties in linear resistive networks, where individual elements are subject to Johnson-Nyquist noise.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728113982
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference58th IEEE Conference on Decision and Control, CDC 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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