2D-ThermAl: Physics-Informed Framework for Thermal Analysis of Circuits using Generative AI

Abstract

Thermal analysis is increasingly critical in modern integrated circuits, where non-uniform power dissipation and high transistor densities can cause rapid temperature spikes and reliability concerns. Traditional methods, such as FEM-based simulations, offer high accuracy but are computationally prohibitive for early-stage design, often requiring multiple iterative redesign cycles to resolve late-stage thermal failures. To address these challenges, we propose ‘2D-ThermAl’, a physics-informed generative AI framework that effectively identifies heat sources and estimates full-chip transient and steady-state thermal distributions directly from input activity profiles. ThermAl employs a hybrid U-Net architecture enhanced with positional encoding and a Boltzmann regularizer to maintain physical fidelity. Our model is trained on an extensive dataset of heat dissipation maps for over 200 circuit configurations, ranging from simple logic gates (e.g., inverters, NAND, XOR) to complex designs, generated using COMSOL and Cadence EDA flows. The dataset captures diverse activity patterns, and we note that material-dependent thermal properties may require targeted fine-tuning to ensure accuracy across different fabrication contexts. Experimental results demonstrate that 2D-ThermAl provides precise temperature mappings for large circuits, with a root mean squared error (RMSE) of only 0.71°C and outperforms conventional FEM tools by running up to ∼ 200× faster. We analyze performance across diverse layouts and workloads and discuss its applicability to large-scale EDA workflows. Although thermal reliability assessments often extend beyond 85°C for post-layout signoff, our focus here is on early-stage hotspot detection and thermal pattern learning. To ensure generalization beyond the nominal operating range (25−55°C), we additionally performed cross-validation on an extended dataset that spans 25−95°C, maintaining a high accuracy (< 2.2% full-scale RMSE) even under elevated temperature conditions representative of the peak power and stress scenarios. Limitations such as 2D-only modeling and real-world validation are addressed with concrete future directions, including 3D extension, generalization across technology nodes, and transfer learning strategies.