Year: 2025
Type: article
Abstract: This paper proposes an improved parameter extraction optimization algorithm for radio frequency (RF) devices. The algorithm integrates parameter classification and correction, gradient-based performance handling, bias-aware updates, and group-based optimization strategies, achieving enhanced optimization accuracy, accelerated convergence, and improved stability. It effectively addresses the limitations of deterministic algorithms in RF device parameter extraction optimization, such as low efficiency, sensitivity to initial values, and unstable convergence. To validate the algorithm's effectiveness, a Ka-band filter performance curve fitting case study was conducted. By comparing simulated curves with optimized fitted curves, the advantages of the algorithm in terms of optimization efficiency, accuracy, and convergence stability were demonstrated. Experimental results show that, compared to traditional optimization algorithms, the proposed method significantly improves curve fitting accuracy, computational efficiency, and stability, highlighting its application value in RF device parameter extraction.
Source: Micromachines
DOI: 10.3390/mi16040432
Institution: Hangzhou Dianzi University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,534
OpenAlex ID: W4409108070