ARO: Autoregressive operator learning for transferable and multi-fidelity 3D-IC thermal analysis with active learning

As 3D integrated circuits (ICs) have emerged as a promising direction in the semiconductor industry, thermal issues in 3D-ICs have become increasingly prominent. In this work, we develop a novel machine learning (ML) thermal analysis framework, namely Autoregressive Operator (ARO), to address the pressing need for rapid yet highly accurate thermal predictions during the chip design process. Unlike traditional ML-based methods that can only deal with scenarios of well-defined input-output domains, ARO learns the thermal diffusion operator such that it can generalize to any unseen circuits and map the power traces to the steady-state/transient thermal spatial-temporal distributions. To further reduce the computational demand of data preparation, we equip ARO with multi-fidelity fusion to exploit the advantage of computationally cheap low-fidelity simulations and expensive high-fidelity simulations and active learning to guide the preparation of training data. Our results show that, for the unseen testing cases, a well-trained ARO can produce accurate results with about 1000× speedup compared to MTA. Moreover, equipped with active learning, ARO achieves at least 25% data reduction compared to pseudo-random strategies.