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A novel frequency domain behavioral modeling method for gallium‐nitride (GaN) devices is presented in this article. By utilizing a multi‐dimensional polynomial function, the proposed technique interpolates DC‐bias voltage and temperature based on the Canonical section‐wise piecewise linear (CSWPL) model framework. A detailed description of the model's theory is provided. With data from 10‐W GaN devices,...
In recent years, gallium nitride (GaN) high electron mobility transistors (HEMTs) have come to the forefront of the semiconductor industry because of their exceptional performance in both high‐power and high‐frequency utility. Accurate capacitance modeling is crucial to optimize performance and facilitate energy‐efficient electronic circuit design. In order to reflect the complex nature of the aluminum...
In this work, an electron blocking layer (EBL) free light emitting diode (LED) nanowire is proposed with alternate prestrained layers of InxGa1−xN/GaN, which are inserted between the GaN/InGaN multi‐quantum wells (MQWs) and n‐GaN layer. This study signifies the role of prestrained layers on the piezoelectric polarization of LED nanowires, for enhanced luminescence. When compared with the conventional...
In this paper, an automatic multi‐objective particle swarm optimization (AMOPSO) method is developed for the design of Doherty power amplifiers (DPAs). In comparison to the well‐known built‐in optimizer available in commercial simulators, the proposed method not only reduces the optimization time, but also provides superior power added efficiency (PAE) for the final power amplifier. According to the...
This paper presents a novel poly‐harmonic distortion (PHD) model that incorporates the DC input and output bias voltages using Gaussian process regression (GPR). Simulation tests were conducted using a 10‐W gallium nitride (GaN) HEMT transistor from Wolfspeed, and the model implementation test was performed in the Keysight Advanced Design System environment. The results showed that the GPR‐based PHD...
In this article, a study of performing machine learning (ML) based modeling for semiconductor devices has been developed using experimental microwave data. Characterization of gallium arsenide (GaAs) pseudomorphic high electron mobility transistors (pHEMTs) with different gate widths is used as the illustrative example to demonstrate the accuracy and effectiveness of the presented modeling procedure...
This work focuses on the equivalent‐circuit modeling of microwave field‐effect transistors. Although the purely direct approach allows obtaining a good prediction accuracy in a straightforward way without the need of any optimization, optimization algorithms are often used to achieve an improved accuracy at the cost of a higher modeling complexity. The pros and cons of empowering the direct approach...
The key objective of this study is to check out the performance of two multilayer processed double‐heterojunction pHEMTs with different gate width as well as one conventional single‐heterojunction HEMT. Different types of investigation have been undertaken by means of on‐wafer DC, small‐signal, and nonlinear two‐tone measurements. The modeling of the DC output characteristics was successfully accomplished...