# Investigation and Modeling of the Behavior of Temperature Characteristics of 0.3–1.1 GHz Complementary Metal Oxide Semiconductor Class-A Broadband Power Amplifiers

## TL;DR

This paper models how temperature affects the performance of a specific type of power amplifier using machine learning techniques.

## Contribution

The study introduces an improved method for modeling temperature characteristics of CMOS class-A PAs using SVM.

## Key findings

- SVM outperforms Elman and GRNN in modeling accuracy and time for PA temperature characteristics.
- Parameters like S11, S12, S21, and S22 are effectively modeled using SVM.
- The approach can be extended to other types of amplifiers and electronic circuits.

## Abstract

A power amplifier (PA) stands as a central module within the electronic information system (EIS), and any variation in a PA’s specifications has a direct impact on the EIS’s performance, especially in the face of temperature fluctuations. In examining the influence of PA specification changes on the EIS, we employed support vector machine (SVM) to model the behavior of the temperature characteristics of 0.3–1.1 GHz complementary metal oxide semiconductor (CMOS) class-A broadband PAs. The results show that the parameters of S11, S12, S21, and S22 can be effectively modeled. SVM outperforms Elman and GRNN in terms of combined modeling time and modeling accuracy. This research can be extended to modeling the behavior of other types of power amplifiers or devices and circuits.