On the Usage of Genetic Algorithms, Reinforcement Learning and Bayesian Optimisation for RF IC Design Automation
Margarida Lourenço, Bing Zhao, Junde Li, Marcelino B. Santos, Pui‐In Mak, Rui Paulo Martins, Wei-Han Yu, Fábio Passos · 2025
The design of Radio Frequency (RF) Integrated Circuits (ICs) is a highly complex and computationally intensive task, requiring precise optimisation of multiple parameters to achieve desired performance. Traditional design methodologies rely heavily on expert knowledge and iterative simulations, leading to prolonged development cycles. In this work, we explore the application of three powerful artificial intelligence (AI)-driven optimisation techniques—Genetic Algorithms (GA), Reinforcement Learning (RL), and Bayesian Optimisation (BO)—to automate and enhance the RF IC design process. We evaluate their effectiveness in optimising key design parameters for a Power Voltage Controlled Oscillator (P-VCO). Due to the difference in the studied techniques, it is not straighforward to compare the performance of these techniques in terms of convergence speed and computational efficiency while achieving significant quality designs. However, the experimental results demonstrated here by all three methods, prove that AI-driven optimisation can significantly reduce design time while achieving superior performance when compared to conventional methods. This work highlights the potential of AI in revolutionising RF IC design automation and paves the way for more efficient and intelligent design methodologies.