Efficient optimization of a Ka-Band MMIC sub-harmonically pumped image rejection diode mixer

Author

Xu, Y. ; Guo, Y. ; Xu, R. ; Yan, B. ; Liu, G.

Author_Institution

Sch. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu

Volume

2

fYear

2008

fDate

21-24 April 2008

Firstpage

862

Lastpage

864

Abstract

An efficient optimization technique, support vector regression (SVR) approach, is proposed for designing of Ka-band MMIC sub-harmonically pumped image rejection diode mixer. This SVR approach comes from the support vector machine (SVM) learning theory, which is based on the structural risk minimization (SRM) principle and leads good generalization ability. With this method, a Ka-band MMIC 4th harmonic image rejection diode mixer is designed using comercial United Monolithic Semiconductors process. Details of design approach and outcome of performance simulations is presented.

Keywords

MMIC mixers; learning (artificial intelligence); minimisation; regression analysis; support vector machines; Ka-Band MMIC sub-harmonically pumped image rejection diode mixer; optimization technique; structural risk minimization principle; support vector machine learning theory; support vector regression; united monolithic semiconductors process; Capacitors; Circuit simulation; Design engineering; Design optimization; MMICs; Mixers; RF signals; Radio frequency; Semiconductor diodes; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Microwave and Millimeter Wave Technology, 2008. ICMMT 2008. International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-1879-4

Electronic_ISBN

978-1-4244-1880-0

Type

conf

DOI

10.1109/ICMMT.2008.4540538

Filename

4540538