Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency Circuit Optimization
<|reference_start|>Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency Circuit Optimization: This paper presents an automated method for optimizing parameters in analog/high-frequency circuits, aiming to maximize performance parameters of a radio-frequency (RF) receiver. The design target includes a reduction of power consumption and noise figure and an increase in conversion gain. This study investigates the use of an artificial algorithm for the optimization of a receiver, illustrating how to fulfill the performance parameters with diverse circuit parameters. To overcome issues observed in the traditional Genetic Algorithm (GA), the concept of the Circuit-centric Genetic Algorithm (CGA) is proposed as a viable approach. The new method adopts an inference process that is simpler and computationally more efficient than the existing deep learning models. In addition, CGA offers significant advantages over manual design of finding optimal points and the conventional GA, mitigating the designer's workload while searching for superior optimum points.<|reference_end|>
@article{kwon2023circuit-centric,
    title={Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency
  Circuit Optimization},
    author={Mingi Kwon, Yeonjun Lee, Ickhyun Song},
    journal={arXiv preprint arXiv:2403.17938},
    year={2023},
    archivePrefix={arXiv},
    eprint={2403.17938},
    primaryClass={cs.NE cs.SY eess.SY}
}