Deep Learning Modeling Method for RF Devices Based on Uniform Noise Training Set

TL;DR

This paper introduces a deep learning approach for RF device modeling using a uniform noise training set, enabling better capture of nonlinear characteristics and improved generalization in RF circuit simulations.

Contribution

It proposes a novel deep learning modeling method with a uniform noise training set for RF devices, enhancing nonlinear characteristic modeling and generalization capabilities.

Findings

The uniform noise training set effectively captures RF device nonlinearities.

The trained model can predict unseen waveform patterns.

The method demonstrates strong generalization and practical value.

Abstract

As the scale and complexity of integrated circuits continue to increase, traditional modeling methods are struggling to address the nonlinear challenges in radio frequency (RF) chips. Deep learning has been increasingly applied to RF device modeling. This paper proposes a deep learning-based modeling method for RF devices using a uniform noise training set, aimed at modeling and fitting the nonlinear characteristics of RF devices. We hypothesize that a uniform noise signal can encompass the full range of characteristics across both frequency and amplitude, and that a deep learning model can effectively capture and learn these features. Based on this hypothesis, the paper designs a complete integrated circuit modeling process based on measured data, including data collection, processing, and neural network training. The proposed method is experimentally validated using the RF amplifier PW210 as a case study. Experimental results show that the uniform noise training set allows the model to capture the nonlinear characteristics of RF devices, and the trained model can predict waveform patterns it has never encountered before. The proposed deep learning-based RF device modeling method, using a uniform noise training set, demonstrates strong generalization capability and excellent training performance, offering high practical application value.