Direct current (DC) analysis lies at the heart of integrated circuit design in seeking DC operating points. Although pseudo-transient analysis (PTA) methods have been widely used in DC analysis in both industry and academia, their initial parameters and stepping strategy require expert knowledge and labor tuning to deliver efficient performance, which hinders their further applications. In this paper, we leverage the latest advancements in machine learning to deploy PTA with more efficient setups for different problems. More specifically, active learning, which automatically draws knowledge from other circuits, is used to provide suitable initial parameters for PTA solver, and then calibrate on-the-fly to further accelerate the simulation process using TD3-based reinforcement learning (RL). To expedite model convergence, we introduce dual agents and a public sampling buffer in our RL method to enhance sample utilization. To further improve the learning efficiency of the RL agent, we incorporate imitation learning to improve reward function and introduce supervised learning to provide a better dual-agent rotation strategy. We make the proposed algorithm a general out-of-the-box SPICE-like solver and assess it on a variety of circuits, demonstrating up to 3.10× reduction in NR iterations for the initial stage and 285.71× for the RL stage.

Pseudo transient analysis (PTA) has been a promising solution for direct current (DC) analysis of transistor-level circuit simulation. Despite its popularity, PTA requires meticulous hyperparameter tuning for optimal performance. In this paper, we propose pseudo adjoint optimization, Soda-PTA, which models the PTA solution curve (which is used to measure convergence) using a neural ordinary differential equation (Neural ODE) and deriving explicit gradients of the Newton-Raphson (NR) iteration w.r.t. the PTA hyperparameters through the classic adjoint method, enabling effective optimization of the PTA hyperparameters. To generalize Soda-PTA for unseen circuits, we further introduce a graph convolution network to transfer optimal PTA hyperparameters from the other circuits to the target one. Soda-PTA is implemented in an out-of-the-box SPICE simulator. Through extensive experiments, Soda-PTA demonstrates superior acceleration performance: an average speedup of 1.53x over the state-of-the-art BoA-PTA while ensuring superior convergence and up to 22.12x speedup compared to the native PTA solver.