TY - GEN
T1 - Gaussian Process surrogate model for variability analysis of RF circuits
AU - Nguyen, Thong
AU - Schutt-Aine, Jose
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - Non-intrusive methods for studying processes involving variables changing such as design optimization, manufacture variation etc. require evaluations of the quantity of interests for a numerous times. These methods, hence, rely on an accurate surrogate model of the process under study. Gaussian Process (GP) is a well-known non-parametric modeling technique for surrogate modeling. This paper explores the effectiveness of GP to model RF applications. The analysis of a milimeter-wave bandpass filter is presented to illustrate the method.
AB - Non-intrusive methods for studying processes involving variables changing such as design optimization, manufacture variation etc. require evaluations of the quantity of interests for a numerous times. These methods, hence, rely on an accurate surrogate model of the process under study. Gaussian Process (GP) is a well-known non-parametric modeling technique for surrogate modeling. This paper explores the effectiveness of GP to model RF applications. The analysis of a milimeter-wave bandpass filter is presented to illustrate the method.
UR - http://www.scopus.com/inward/record.url?scp=85099786347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099786347&partnerID=8YFLogxK
U2 - 10.1109/EDAPS50281.2020.9312886
DO - 10.1109/EDAPS50281.2020.9312886
M3 - Conference contribution
AN - SCOPUS:85099786347
T3 - IEEE Electrical Design of Advanced Packaging and Systems Symposium
BT - Proceedings - IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2020
Y2 - 14 December 2020 through 16 December 2020
ER -