ArrivalFM-OTA: A Large-Scale Benchmark for Physical Representation Learning in Analog Circuit Design

Surrogate modeling in analog integrated circuit design is critically obstructed by stiff nonlinear differential-algebraic equations, discrete device sizing, and a scarcity of open, standardized benchmarks and datasets. To bridge this gap, we introduce ArrivalFM-OTA, a forward modeling (FM) benchmark and dataset for physical representation learning in analog circuit design. ArrivalFM-OTA comprises over 2 million industrial-grade Cadence Spectre simulation datapoints across 28 operational transconductance amplifier (OTA) topologies and 10 FinFET technology nodes from 7nm to 20nm, evaluated under continuous environmental sweeps. On top of this data, ArrivalFM-OTA establishes a surrogate modeling challenge across 4 standardized distribution splits and 68 joint prediction targets spanning validity flags, global performance metrics, and internal transistor operating states. Evaluating representative neural baselines across extensive multi-seed initializations, we propose DAWN (DC Operating Point Analog Waypoint Nodalization), a nodal-conditioned residual framework that improves global metric prediction across all backbones by up to +0.055 R², with our leading model reaching R² ≈ 0.793 in-distribution and R² ≈ 0.764 on an unseen technology node. However, performance drops to R² ≈ 0.281 on unseen topologies, revealing that while neural models achieve competitive interpolation across continuous physical parameters, structural extrapolation across circuit graphs remains a fundamental open challenge for physical representation learning. Finally, we open-source all data, benchmark protocols, multi-seed baseline profiles, and our simulation orchestration pipeline to provide a standardized foundation for transistor-level machine learning and circuit design workflows.