Machine learning in physical design

Abstract

Machine learning is a highly effective instrument in constructing models that can expeditiously produce accurate prognostications. As the complexity of integrated circuit design continues to increase and process nodes continue to evolve, and physical design faces more challenges from modeling and optimization. To address these challenges, machine learning has been introduced into physical design. Thus, in this paper, we discuss the application of machine learning in physical design, covering topics such as Clock Tree Synthesis (CTS), Placement and Routing, IR-Drop and Static Timing Analysis (STA). The essay explores how machine learning can be used to overcome challenges in these areas, such as reducing peak current and clock skew in CTS, optimizing placement parameters and decision-making, predicting routability and reducing IR-drop effects. This paper also discusses various machine learning techniques (ML), such as reinforcement learning, convolutional neural networks and transfer learning. To conclude, we provide insights into how machine learning can be applied to improve various aspects of physical design.