A Nonlinear Behavioral Modeling Approach for Voltage-controlled Oscillators Using Augmented Neural Networks

Huan Yu, Madhavan Swaminathan, Chuanyi Ji, David White

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

13 Scopus citations

Abstract

This paper describes a method to model the nonlinear time-domain steady-state behavior of voltage-controlled oscillators (VCOs) using augmented neural networks. In the proposed method, a feed forward neural network (FFNN) with a periodic unit is used to capture the periodicity of the oscillatory output waveform. Inside the periodic unit, a second FFNN is used to map the control voltage to the instantaneous frequency. As opposed to the state space model which is based on a system of differential equations, the output of the oscillator is generated explicitly using the neural network presented in this paper. The model is trained using the data obtained from the simulation of transistor-level circuit models. The fidelity and speed-up of the model is demonstrated by an example of a transistor-level VCO. The proposed model is compatible with Verilog-A.

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 IEEE/MTT-S International Microwave Symposium, IMS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages551-554
Number of pages4
ISBN (Print)9781538650677
DOIs
StatePublished - Aug 17 2018
Event2018 IEEE/MTT-S International Microwave Symposium, IMS 2018 - Philadelphia, United States
Duration: Jun 10 2018Jun 15 2018

Publication series

NameIEEE MTT-S International Microwave Symposium Digest
Volume2018-June
ISSN (Print)0149-645X

Conference

Conference2018 IEEE/MTT-S International Microwave Symposium, IMS 2018
Country/TerritoryUnited States
CityPhiladelphia
Period6/10/186/15/18

All Science Journal Classification (ASJC) codes

  • Radiation
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A Nonlinear Behavioral Modeling Approach for Voltage-controlled Oscillators Using Augmented Neural Networks'. Together they form a unique fingerprint.

Cite this