Nano Science and Technology InstituteNano Science and Technology Institute
Nano Science and Technology Institute 2005 NSTI Nanotechnology Conference & Trade Show
Nanotech 2005
Bio Nano 2005
Business & Investment
Nano Impact Workshop
Program
Sessions
Sunday
Monday
Tuesday
Wednesday
Thursday
Index of Authors
Index of Keywords
Keynote Presentations
Confirmed Speakers
Participating Companies
Industry Focus Sessions
Nanotech Expo
Special Symposia
Conferences
Sponsors
Exhibitors
Venue 2005
Organization
Press Room
Subscribe
Site Map
 
Nanotech 2005 At A Glance
Nanotech Proceedings
Nanotechnology Proceedings
Global Partner
nano tech
Supporting Organizations
Nanotech 2005 Supporting Organization
Media Sponsors
Nanotech 2005 Medias Sponsors
Event Contact
696 San Ramon Valley Blvd., Ste. 423
Danville, CA 94526
Ph: (925) 353-5004
Fx: (925) 886-8461
E-mail:
 
 

Computer Aided Design and Optimization of Integrated Circuits with RF MEMS Devices by an ANN Based Macro-Modeling Approach

Y. Lee, Y. Park, F. Niu, B. Bachman and D. Filipovic
University of Colorado, US

Keywords:
RF MEMS, ANN, macro-modeling, circuit simulator

Abstract:
Computer Aided Design of RF MEMS enables a reliable, cost effective and time saving alternative to the experiment guided development process. Full-wave analysis techniques including the finite element method and finite difference time domain have been applied and good results were obtained. However, a large memory/time overhead and inability for effective modeling on a system level limit their use for primarily individual device level analysis. In this paper, artificial neural network based macro-modeling approach for design and optimization of integrated circuits with RF MEMS resonators is proposed. Proposed approach combines the finite element analysis and electromechanical analogies to obtain an ANN trained blackbox model, which is fully compatible for analysis, tuning, design and optimization in a selected circuit simulator environment. The ANN algorithm is the centerpiece of this approach since it provides the intelligence for the model, i.e., mapping the device geometry dataset into corresponding output performance dataset. Although ANNs require extensive time and effort needed to prepare the training dataset, once the ANN is trained, it predicts unknown input/output relationships with significant savings in time/cost/effort while retaining the accuracy of full-wave analysis.

Back to Program

Sessions Sunday Monday Tuesday Wednesday Thursday Authors

Nanotech 2005 Conference Program Abstract

 
Gold Sponsors
Nanotech Gold Sponsors
Silver Sponsors
Nanotech Silver Sponsors
Gold Key Sponsors
Nanotech Gold Key Sponsors
Nanotech Ventures Sponsors
Nanotech Ventures Sponsors
Sponsors
Nanotech Sponsors
News Headlines
NSTI Online Community
 
 

© Nano Science and Technology Institute, all rights reserved.
Terms of use | Privacy policy | Contact