ThunderGen: A Comprehensive Dataset and Multi-Topology Synthetic Estimator for Fast Analog Circuit Simulation

Considering the design of analog integrated circuits (ICs), SPICE-level simulations remain essential for final verification, while their prohibitive computational requirements motivated the research towards synthetic performance estimators to accelerate early-stage design space exploration. However, these surrogates frequently suffer from a significant accuracy gap, failing to capture the non-linearities and high-dimensional complexities of analog circuits. This is mainly due to the lack of high-fidelity datasets covering vast architectural design spaces. Existing works primarily focus on fixed topologies and parameter sweeps, hindering the development of robust machine learning models capable of true topological generalization. This paper presents ThunderGen, a publicly available comprehensive framework and corresponding dataset conceived to bridge the gap between simulation speed and estimation accuracy. ThunderGen introduces a large-scale, multi-topology dataset generated through an automated pipeline that spans diverse circuit families, providing a rigorous ground truth. Alongside this dataset, we propose a performance estimator that serves as a high-fidelity benchmark to replace time consuming SPICE-level simulations and enabling AI-driven topology searches in early design phases. The experimental campaign evaluated ThunderGen against two state-of-the-art methodologies considering a realistic dataset consisting of more than $44,000$ circuits organized in five circuit families and thousands of circuit topologies per each. Experimental results demonstrate that ThunderGen outperforms existing estimators by $4.69\times$ and $6.07\times$ respectively, also showing an average relative percentage error compared to SPICE simulation limited to $16.11$%.