EMSim+: Accelerating Electromagnetic Security Evaluation With Generative Adversarial Network and Transfer Learning

Abstract

Electromagnetic side-channel analysis (EM SCA) attack poses a serious threat to integrated circuits (ICs), necessitating timely vulnerability detection before deployment to enhance EM side-channel security. Various EM simulation methods have emerged for analyzing EM side-channel leakage, providing sufficiently accurate results. However, these simulator-based methods still face two principal challenges in the design process of high security chips. Firstly, the large volume of measurement data required for a single security evaluation results in substantial time overhead. Secondly, design iterations lead to repetitive security evaluations, thus increasing the evaluation cost. In this paper, we propose EMSim+ which includes two efficient and accurate layout-level EM side-channel leakage evaluation frameworks named EMSim+GAN and EMSim+GAN+TL to mitigate the above challenges, respectively. EMSim+GAN integrates a Generative Adversarial Network (GAN) model that utilizes the chip’s cell current and power grid information to predict EM emanations quickly. EMSim+GAN+TL further incorporates transfer learning (TL) within the framework, leveraging the experience of existing designs to reduce the training datasets for new designs and achieve the target accuracy. We compare the simulation results of EMSim+ with the state-of-the-art EM simulation tool, EMSim as well as silicon measurements. Experimental results not only prove the high efficiency and high simulation accuracy of EMSim+, but also verify its generalization ability across different designs and technology nodes.