Machine Learning Applications in Electronic Design Automation

Introduction.- Analysis of Digital Design: Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning.- RouteNet: Routability Prediction for Mixed-size Designs Using Convolutional Neural Network.- High Performance Graph Convolutional networks with Applications in Testability Analysis.- MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification.- GRANNITE: Graph Neural Network Inference for Transferable Power Estimation.- Machine Learning-Enabled High-Frequency Low-Power Digital Design Implementation at Advanced Process Nodes.- Optimization of Digital Design: Chip Placement with Deep Reinforcement learning.- DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement.- TreeNet: Deep Point Cloud Embedding for Routing Tree Construction.- Asynchronous Reinforcement Learning Framework for Net Order Exploration in Detailed Routing.- Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes.- PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning.- GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization.- Analysis and Optimization of Analog Design: Machine Learning Techniques in Analog Layout Automation.- Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks.- ParaGraph: Layout parasitics and device parameter prediction using graph neural network.- GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learn.- Parasitic-Aware Analog Circuit Sizing with Graph Neural Networks and Bayesian Optimization.- Logic and Physical Verification: Deep Predictive Coverage Collection/ Dynamically Optimized Test Generation Using Machine Learning.- Novelty-Driven Verification: Using Machine Learning to Identify Novel Stimuli and Close Coverage.- Using Machine Learning Clustering To Find Large Coverage Holes.- GAN-OPC: Mask optimization with lithography-guided generative adversarial nets.- Layout hotspot detection with feature tensor generation and deep biased learning.