Automated analog circuit design migration significantly alleviates the burden on designers in circuit sizing under various operating conditions. Conventional methods model the migration problem as black-box optimization, requiring excessive iterations of costly simulations to converge. Reinforcement learning exhibits significant promise in transfer learning, as it enables the generation of circuits that fulfill specifications efficiently. The paper proposes a novel value decomposition-based multi-agent reinforcement learning framework, aiming to model complex analog circuits and eliminate the need for manually defined specifications of sub-circuits for new operating conditions. Additionally, it incorporates domain randomization techniques to efficiently generate circuits that meet unforeseen scenarios with minimal simulations. Experiment demonstrates that our algorithm can efficiently generate circuits meeting specifications under new operating conditions in few number of steps, outperforming state-of-the-art methods.

We propose a pyramid scene parsing network (PSPN) with skip-connection architecture to effectively utilize physical information that characterizes IR drop distribution, including current source locations, via locations, and asymmetric topological connections, achieving highly accurate IR drop prediction for power delivery networks (PDN) of varying scales, even with a limited dataset. Skip-connection architecture preserves the positional information of current sources, which often correlates with large IR drop, facilitating the identification of hotspots. We incorporate via locations into the model to effectively describe the topological connection distance between voltage sources and different nodes in the multi-layer PDN, while the traditional method only considers the horizontal distance between nodes and voltage sources, which is invalid for prediction. To capture asymmetric connection features within the PDN efficiently, we introduce a shape-adaptive convolutional kernel to solve the problem of inadequate extraction of feature information in a traditional method. Finally, we propose a loss function with Kirchhoff's law constraints to ensure the model's prediction aligns with the electrical characteristics of the circuit, which can't be guaranteed by traditional machine learning-based methods only taking the prediction accuracy into consideration. Our results, based on training with only 100 synthetic circuits, demonstrate the superiority of our method over the state-of-the-art prediction technique. Across evaluations on 10 real circuits, our approach consistently delivers a 50 % improvement in precision.