An Efficient and Accurate Approach for Characterizing Non-Linear Capacitance in MESFET/HEMT Devices

Autor:	Aderinto J. Ogunniyi, Stanley L. Henriquez, Caroline Karangu, C. White
Rok vydání:	2006
Předmět:	Artificial neural network business.industry Computer science Electrical engineering Gallium nitride High-electron-mobility transistor Capacitance chemistry.chemical_compound Nonlinear system chemistry Electronic engineering Silicon carbide Feedforward neural network MESFET business
Zdroj:	2006 IEEE Sarnoff Symposium.
DOI:	10.1109/sarnof.2006.4534775
Popis:	This paper presents an accurate and efficient approach of characterizing the non-linear capacitance in MESFET/HEMT devices. This approach utilizes a feed-forward neural network program using the back-propagation method with the Levenberg-Marquardt (LM) algorithm implemented. Using the LM algorithm provides accurate modeling of the non-linear capacitance with minimal training time. This approach is truly independent of device process and technology, and can therefore be applicable to FET devices from SiC, GaN, MOSFETs to GaAs and InP HEMTs. Excellent agreement is also observed between the novel approach and measured results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9eda7bda957d399203f64cbcda430a36 https://doi.org/10.1109/sarnof.2006.4534775 Zobrazit plný text záznamu