A Robust Method for Intrinsic Parameter Extraction of GaN HEMT Small-signal Model Combining PCA and OOA Algorithms

A method for extracting intrinsic parameters by combining the Principal Component Analysis (PCA) algorithm with the Osprey Optimization Algorithm (OOA) is proposed in this paper. Traditional methods for extracting intrinsic parameters from Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) face challenges, such as long S-parameter modeling times and low accuracy. To improve model accuracy, the OOA algorithm can be used to optimize the intrinsic parameters. However, the OOA algorithm has drawbacks, such as a tendency to fall into local optima and high sensitivity to parameters. Additionally, it does not account for correlations between intrinsic parameters, which limits improvements in model accuracy. To further enhance the model’s precision, PCA is applied in this paper to strengthen the correlation between intrinsic parameters. The PCA-processed intrinsic parameters are then combined with the OOA algorithm for extraction. Experimental results show that over the wide frequency range of 0.5–20.5 GHz, the PCA-OOA algorithm significantly improves S-parameter modeling accuracy compared to traditional extraction techniques, offering a new approach for efficient and accurate modeling of GaN HEMT devices.