Results (3)
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Keyword: amplifiers
An Advanced Load-Line Analysis Software for use in the Design and Simulation of Microwave Low-Distortion, High-Efficiency and High-Power GaN HEMT Amplifiers
by Yasushi Itoh, Takana Kaho and Koji Matsunaga
Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # 14-21, 2023; DOI: 10.55708/js0204002
Abstract: An advanced load-line analysis software is devised for nonlinear circuit design and simulation of microwave low-distortion, high-efficiency and high-power GaN HEMT amplifiers. A single software package can incorporate DC, small- and large-signal performances of GaN HEMT devices, and then analyze nonlinear performance of amplitude-to-amplitude (AM-AM) and amplitude-to-phase (AM-PM) modulations, and finally evaluate intermodulation distortion (IMD)… Read More
(This article belongs to the Section Electronic Engineering (EEE))
A Review of Machine Learning Techniques for Analog Integrated Circuit Design and Optimization
by Grosu Vasile
Journal of Engineering Research and Sciences, Volume 5, Issue 9, Page # 15-34, 2026; DOI: 10.55708/js0509002
Abstract: Analog electronic design automation has historically lagged behind its digital counterpart due to the complex, multi-dimensional design space and strict sensitivity to parasitic effects, process variations, and geometric constraints. A comprehensive review of the evolution, current state-of-the-art, and future trajectories of electronic design automation tools specifically tailored for analog integrated circuit design could serve as… Read More
(This article belongs to the Section Electronic Engineering (EEE))
Metamodel based Optimization for Analog Integrated Circuits
by Vasile Grosu, Nicolae Patache and Emilian David
Journal of Engineering Research and Sciences, Volume 5, Issue 7, Page # 1-15, 2026; DOI: 10.55708/js0507001
Abstract: In recent times, machine learning applications have become an important component of the analog integrated circuit development cycle. Several phases such as circuit sizing, optimization or pre-silicon verification have benefited from machine learning automation. For developing such machine learning models, a certain amount of training and testing samples needs to be acquired through circuit simulations.… Read More
(This article belongs to the Special Issue on SP8 (Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26)) and the Section Electronic Engineering (EEE))