Deep Learning-Based Device Twin Model and Its Testing Applications

With the increasing complexity of Radio Frequency Integrated Circuits (RFIC), the difficulty and cost of testing them have also significantly increased. Effective modeling can reduce testing costs. However, traditional SPICE models are not easy to obtain, and S-parameter testing is expensive and time-consuming. In order to achieve simple and accurate modeling of RF devices, this article takes the RF power amplifier PW210 as an example to explore the application of deep learning methods in RF device modeling. This article proposes a new method for twin modeling of RF devices and matches it with appropriate data processing, sampling, model training, and validation methods for testing and application, demonstrating the feasibility of this method. In addition, this article uses TPE and ENAS methods to search for the structure of the constructed residual network, compares their efficiency and accuracy, and finds a model structure suitable for PW210 RF power amplifier.