Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates
In this dissertation, a predictive (scalable) measurement-based PEEC modeling method for high-frequency interconnects on low-cost FR4 substrates is proposed and demonstrated. The interconnects are modeled with equivalent circuits of scalable building blocks using a rapid and accurate optimization me...
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ndltd-GATECH-oai-smartech.gatech.edu-1853-71082013-01-07T20:12:16ZRapid, Predictive Modeling for High Frequency Interconnect on Low Cost SubstratesShin, JaeminModelingHigh frequencyInterconnectMeasurementPredictiveFR4In this dissertation, a predictive (scalable) measurement-based PEEC modeling method for high-frequency interconnects on low-cost FR4 substrates is proposed and demonstrated. The interconnects are modeled with equivalent circuits of scalable building blocks using a rapid and accurate optimization method to fit parameter data up to 10 GHz. The predictive power of the developed scalable models is demonstrated in several extended interconnect structures and the ability to use interpolation to predict the high frequency performance of structures with differently sized building blocks is demonstrated. The usefulness of the proposed modeling method is validated by comparing predictions to measurements both in frequency domain and in time domain. The efficiency and accuracy of the method are also compared with the Advanced Design System (ADS) momentum simulation tool. The results show that this proposed high-frequency interconnect modeling method is very much more efficient in terms of simulation time, while maintaining comparable accuracy, compared to momentum simulations and measured behavior.Georgia Institute of Technology2005-09-16T14:58:34Z2005-09-16T14:58:34Z2005-05-13Dissertation15732925 bytesapplication/pdfhttp://hdl.handle.net/1853/7108en_US |
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Modeling High frequency Interconnect Measurement Predictive FR4 |
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Modeling High frequency Interconnect Measurement Predictive FR4 Shin, Jaemin Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates |
description |
In this dissertation, a predictive (scalable) measurement-based PEEC modeling method for high-frequency interconnects on low-cost FR4 substrates is proposed and demonstrated. The interconnects are modeled with equivalent circuits of scalable building blocks using a rapid and accurate optimization method to fit parameter data up to 10 GHz. The predictive power of the developed scalable models is demonstrated in several extended interconnect structures and the ability to use interpolation to predict the high frequency performance of structures with differently sized building blocks is demonstrated. The usefulness of the proposed modeling method is validated by comparing predictions to measurements both in frequency domain and in time domain. The efficiency and accuracy of the method are also compared with the Advanced Design System (ADS) momentum simulation tool. The results show that this proposed high-frequency interconnect modeling method is very much more efficient in terms of simulation time, while maintaining comparable accuracy, compared to momentum simulations and measured behavior. |
author |
Shin, Jaemin |
author_facet |
Shin, Jaemin |
author_sort |
Shin, Jaemin |
title |
Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates |
title_short |
Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates |
title_full |
Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates |
title_fullStr |
Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates |
title_full_unstemmed |
Rapid, Predictive Modeling for High Frequency Interconnect on Low Cost Substrates |
title_sort |
rapid, predictive modeling for high frequency interconnect on low cost substrates |
publisher |
Georgia Institute of Technology |
publishDate |
2005 |
url |
http://hdl.handle.net/1853/7108 |
work_keys_str_mv |
AT shinjaemin rapidpredictivemodelingforhighfrequencyinterconnectonlowcostsubstrates |
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1716474270402478080 |