ML assisted circuit design using active learning

Abstract

This thesis explores the use of active learning (AL) to reduce the amount of training data required for machine learning (ML) models used for circuit design by selective sampling of the most informative data points. In this study, an uncertainty-based AL approach was implemented. This method leverages the model’s prediction uncertainty to selectively sample the most informative data points. A convolutional neural network (CNN) is trained to predict scattering parameters (S-parameters) from pixelated representations of 2-port passive microwave circuits. This method enables the ML model to act as a fast surrogate to electromagnetic (EM) solvers. The goal in this project is to speed up the training process for the ML models using AL. The performance of the AL-based model is compared to a baseline model trained using random sampling. Evaluation is conducted on a fixed test set, as well as across different frequency ranges and S-parameter. Results show that AL consistently outperforms the baseline in terms of root mean square error (RMSE), particularly at higher frequencies where EM behavior becomes more complex. Ensemble models were also investigated to assess their potential in improving the sampling strategy. However, they did not yield better results. Each ensemble run required over two weeks of computation, limiting further experimentation. An ensemble of models refers to a collection of multiple individual models whose predictions are combined to improve overall performance and robustness. Finally, the models were tested in a design task where a genetic algorithm generated circuits from targeted S-parameters. The AL model achieved a 32.9% lower mean RMSE than the baseline when comparing predicted and simulated S-parameters. These findings highlight AL as a promising approach for improving data efficiency in ML-based circuit design.