Machine Learning Driven Advanced Packaging and Miniaturization of IoT for Wireless Power Transfer Solutions

Increasing application scope of Internet of Things (IoT) devices have resulted in strict design requirements such as compact systems and efficient power delivery architectures to reduce battery wastage. RF Wireless Power Transfer (WPT) have shown to be promising to address these issues, but with the cost of increased design complexity. In this work, we use machine learning based optimization to select an optimal set of control parameters of a WPT architecture and miniaturize the system while increasing the overall RF-DC conversion efficiency.