DC analysis holds critical importance in nonlinear circuit simulation, providing the essential precondition for transient and AC analyses. While Pseudo-Transient Analysis (PTA) and its variants excel in DC analysis, selecting the optimal PTA method for specific circuits remains challenging. To address this, we propose GE-LLM, a novel framework for optimal PTA method selection, which integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs). The framework first converts circuit netlists into graph representations and employs a GNN-based graph encoder to capture essential circuit topologies. Subsequently, a novel text-graph alignment strategy bridges circuit topologies and textual descriptions, enabling the LLM to effectively comprehend multimodal information. Finally, we introduce a multi-perspective few-shot prompt that mitigates data scarcity by enabling effective in-context learning from limited circuit examples. Experimental results demonstrate that GE-LLM achieves a high selection accuracy of 0.9714 and improves the efficiency of DC analysis, yielding an average speedup of 2.89× in PTA steps (up to 12.14×) and 3.45× in Newton-Raphson iterations (up to 30.39×) compared to a commercial SPICE-like simulator.

Advanced integrated circuit (IC) systems increasingly utilize chiplet-based packaging with complex $2.5 \mathrm{D} / 3 \mathrm{D}$ structures and dense Through-Silicon Via (TSV) arrays. While the Finite Element Method (FEM) provides high-fidelity thermal simulation for these systems, its computational efficiency degrades significantly when generating and optimizing meshes for intricate geometries. To address these performance limitations while preserving simulation accuracy, we present NeuralMesh, a novel framework that accelerates thermal analysis of chiplet-based ICs. Our approach integrates deep learning and geometric analysis to optimize mesh generation without the need for iterative refinement steps. NeuralMesh first employs an enhanced segmentation model to predict thermal distributions based on geometric, material, and power parameters. These predictions, combined with key geometric features, guide the optimization of an initial coarse FEM mesh. By eliminating traditional iterative mesh refinement, our framework achieves up to $45.00 \times$ mesh generation speedup while maintaining thermal accuracy within 0.8% of commercial COMSOL simulations. It reduces the number of mesh elements in unimportant areas, which represents a speed improvement of the subsequent thermal simulation. This advancement enables rapid yet precise thermal analysis essential for modern IC package design.

IR drop analysis is crucial for ensuring the reliability and performance of integrated circuits (ICs) but poses computational challenges as the IC designs grow larger, especially for ultra deep-submicron VLSI designs. Deep learnings (DL) as the efficiency-promising solutions, mainly employ various CNN-based networks to achieve image-to-image IR drop predictions. However, they neglect and lose the power delivery network (PDN) global spatial features and cell instance topological information. This paper proposes a novel image-graph heterogeneous fusion framework (IGHF), which integrates the effectiveness and complementarity of dual branches (CNN and GNN) for higher prediction performance. In the CNN-based Power ScaleFusion Unet branch, the proposed long-range and local-detail encoder (LLE) integrates seamlessly with the hierarchical and adjacent compensation group (HACG) module. This design facilitates effective multi-scale global-to-local spatial power feature extraction within the PDN and enables adaptive high-to-low-level feature fusion and compensation in the decoder. Moreover, a cell voltage aware (CVA) module in the GNN branch is designed to adaptively aggregate PDN topological features of heterogeneous neighbors of different orders. Comparative experiments demonstrate that the proposed IGHF achieves significant accuracy improvements, outperforming the state-of-the-art MAUNet and widely-used IREDGe methods by considerable margins of 24.6% and 55.0% reduction in prediction error, while the prediction maps possess higher structural fidelity. Transfer experiments indicate that IGHF with transfer learning can improve the accuracy in real circuits with the few-shot real circuit test cases.

In the post-Moore era, 2.5D chiplet-based ICs present significant challenges in thermal management due to increased power density and thermal hotspots. Neural network-based thermal prediction models can perform real-time predictions for many unseen new designs. However, existing CNN-based and GCN-based methods cannot effectively capture the global thermal features, especially for high-frequency components, hindering pre-diction accuracy enhancement. In this paper, we propose a novel frequency-spatial dual domain aware prediction network (FSA-Heat) for fast and high-accuracy thermal prediction in 2.5D ICs. It integrates high-to-low frequency and spatial domain encoder (FSTE) module with frequency domain cross-scale interaction module (FCIFormer) to achieve high-to-low frequency and global-to-local thermal dissipation feature extraction. Additionally, a frequency-spatial hybrid loss (FSL) is designed to effectively attenuate high-frequency thermal gradient noise and spatial mis-alignments. The experimental results show that the performance enhancements offered by our proposed method are substantial, outperforming the newly-proposed 2.5D method, GCN+PNA, by considerable margins (over 99% RMSE reduction, 4.23X inference time speedup). Moreover, extensive experiments demonstrate that FSA-Heat also exhibits robust generalization capabilities.

Thermal management in large-scale unstructured 2.5D ICs faces the challenges due to the integration of complex geometries and heterogeneous materials. Existing deep learning (DL) methods urgently require a memory-efficient and high-fidelity unstructured representation method for multiscale complex ICs to simultaneously model macroscopic components and microscopic structure. Moreover, it further needs to achieve multiscale geometric thermal feature capture and thermal distribution difference adaptation among heterogeneous materials. Combining a multiscale unstructured point-cloud representation, this paper introduces Therm-PCT, a geometry-material aware point-cloud transformer framework to achieve high-accuracy thermal and its gradient prediction. Therm-PCT incorporates three key modules: adaptive multipath-coupled diffusion (AMD), a wavelet-based fine-grained recovery (WFR), and a thermal-aware Mixture-of-Material-Experts (TA-MoME) adapter. AMD adaptively learns heat diffusion path interaction with serialization-gate-based attention. Furthermore, the WFR module recovers fine-grained thermal gradients through high-frequency wavelet domain enhancement, and the TA-MoME adapter adapts to heterogeneous material by dynamically routing material-specific experts. Experiments demonstrate that the Thermal-PCT’s accuracy performance metric improvements are substantial, outperforming the newly proposed method FSA-Heat, by considerable margins of 78.03%, 84.00%, 67.61%, and 78.25% in 80 K-scale point clouds. It also achieves a 147× speed-up compared to the commercial software COMSOL. Additionally, Therm-PCT shows the potential of zero-shot generalization up to 0.4 M-scale points (5.7× than training scale) and robust performance on unseen geometric shapes.