TY - GEN
T1 - A machine learning methodology for inferring network S-parameters in the presence of variability
AU - Ma, Xiao
AU - Raginsky, Maxim
AU - Cangellaris, Andreas C.
PY - 2018/6/29
Y1 - 2018/6/29
N2 - This paper proposes the use of Variational Autoencoders, a generative modeling technique, for the problem of inferring S-parameters of linear multiport networks in the presence of manufacturing variability. The Variational Autoencoder learns the underlying data generation process and yields a generative network that can approximately mimic the probability distribution of the training data. The generated samples can be used for subsequent statistical simulations. A post-processing step, applying Vector Fitting to the predicted S-parameters, constrains the model to a finite-order rational function form and enforces appropriate physical constraints. The method is validated through its application to a coupled micro strip transmission line.
AB - This paper proposes the use of Variational Autoencoders, a generative modeling technique, for the problem of inferring S-parameters of linear multiport networks in the presence of manufacturing variability. The Variational Autoencoder learns the underlying data generation process and yields a generative network that can approximately mimic the probability distribution of the training data. The generated samples can be used for subsequent statistical simulations. A post-processing step, applying Vector Fitting to the predicted S-parameters, constrains the model to a finite-order rational function form and enforces appropriate physical constraints. The method is validated through its application to a coupled micro strip transmission line.
KW - Bayes methods
KW - Inference algorithms
KW - Integrated circuit interconnections
KW - Statistical analysis
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85050505565&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050505565&partnerID=8YFLogxK
U2 - 10.1109/SaPIW.2018.8401643
DO - 10.1109/SaPIW.2018.8401643
M3 - Conference contribution
AN - SCOPUS:85050505565
T3 - 2018 IEEE 22nd Workshop on Signal and Power Integrity, SPI 2018 - Proceedings
SP - 1
EP - 4
BT - 2018 IEEE 22nd Workshop on Signal and Power Integrity, SPI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE Workshop on Signal and Power Integrity, SPI 2018
Y2 - 22 May 2018 through 25 May 2018
ER -