Behavioral-level modeling of GaN HEMT small-signal intrinsic noise based on DE-SVR algorithm. (May 2022)

• Record Type: Journal Article
• Authors: Wang, Shaowei; Zhang, Jincan; Yang, Shi; Li, Na; Wang, Jinchan; Liu, Min
• Is Part Of: Microelectronics journal. Volume 123(2022)
• Publication Date: 2022-05
• Subjects: DE-SVR algorithm -- BP algorithm -- SVR algorithm -- GaN HEMT small-Signal intrinsic noise model; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2022.105426
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: The Differential Evolution-Support Vector Regression (DE-SVR) algorithm is designed to model the small-signal intrinsic noise behavior of GaN HEMT. It not only overcomes the local minimization shortcoming of the Back Propagation (BP) algorithm, but also uses the DE (Differential Evolution) algorithm to obtain the best parameter c (punishment factor) and parameter g (variance of kernel function) of the Support Vector Regression (SVR) algorithm. In order to validate the superiority of the DE-SVR algorithm, the experiment compares the modeling effects of BP algorithm, SVR algorithm, and DE-SVR algorithm in modeling the small-signal intrinsic noise model of GaN HEMT. The experimental results show that there are obvious advantages for the DE-SVR algorithm in modeling the small-signal intrinsic noise characteristics of GaN HEMT. Highlights: The Differential Evolution-Support Vector Regression (DE-SVR) algorithm overcomes the local minimization shortcoming of the Back Propagation (BP) algorithm. The Differential Evolution-Support Vector Regression (DE-SVR) algorithm uses the DE (Differential Evolution) algorithm to obtain the best parameter c (punishment factor) and parameter g (variance of kernel function) of the Support Vector Regression (SVR) algorithm.