Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering

A Symbolic Method for Distortion Analysis and Optimization

Authors
J. Chen, G. Shi, A. Zhang
Corresponding Author
J. Chen
Available Online July 2015.
DOI
10.2991/aiie-15.2015.118How to use a DOI?
Keywords
distortion analysis; operational amplifier (opamp); optimization; symbolic analysis
Abstract

A symbolic method is presented for calculation of the weakly nonlinear distortion effect of operational amplifiers. To avoid complicated symbolic generation by considering all transistor nonlinearities, a simplified analysis method is adopted. This method identifies the nonlinearity of each amplifier stage by a low-order polynomial. The proposed symbolic method can derive analytical distortion results automatically without the need of going through lengthy signal-flow graph analysis. This method is applied to sweep analysis of the opamp distortion with respect to the external feedback elements, by which optimal selection of the parameter value can be determined at any given frequency. Remarkable speedup over repeated Spectre simulation has been observed.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
10.2991/aiie-15.2015.118
ISSN
1951-6851
DOI
10.2991/aiie-15.2015.118How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - J. Chen
AU  - G. Shi
AU  - A. Zhang
PY  - 2015/07
DA  - 2015/07
TI  - A Symbolic Method for Distortion Analysis and Optimization
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 433
EP  - 436
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.118
DO  - 10.2991/aiie-15.2015.118
ID  - Chen2015/07
ER  -