IR-drop is a critical power integrity challenge in modern VLSI designs that can cause timing degradation, reliability issues, and functional failures if not detected early in the design flow. Conventional IR-drop analysis relies on physics-based signoff tools, which provide high accuracy but incur significant computational cost and require near-final layout information, making them unsuitable for rapid early-stage design exploration. In this work, we propose a deep learning-based surrogate modeling approach for early-stage IR-drop estimation using a CNN. The task is formulated as a dense pixel-wise regression problem, where spatial physical layout features are mapped directly to IR-drop heatmaps. A U-Net-based encoder-decoder architecture with skip connections is employed to effectively capture both local and global spatial dependencies within the layout. The model is trained on a physics-inspired synthetic dataset generated by us, which incorporates key physical factors including power grid structure, cell density distribution, and switching activity. Model performance is evaluated using standard regression metrics such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Experimental results demonstrate that the proposed approach can accurately predict IR-drop distributions with millisecond-level inference time, enabling fast pre-signoff screening and iterative design optimization. The proposed framework is intended as a complementary early-stage analysis tool, providing designers with rapid IR-drop insight prior to expensive signoff analysis. The implementation, dataset generation scripts, and the interactive inference application are publicly available at: https://github.com/riteshbhadana/IR-Drop-Predictor. The live application can be accessed at: https://ir-drop-predictor.streamlit.app/.

翻译：IR压降是现代超大规模集成电路设计中关键的电源完整性挑战，若在设计流程早期未能检测，可能导致时序退化、可靠性问题及功能失效。传统的IR压降分析依赖于基于物理的签核工具，虽能提供高精度，但计算成本高昂且需接近最终的版图信息，使其不适用于快速的早期设计探索。本研究提出一种基于深度学习的代理建模方法，利用CNN进行早期IR压降估计。该任务被构建为密集像素级回归问题，将空间物理版图特征直接映射至IR压降热图。采用基于U-Net的编码器-解码器架构与跳跃连接，以有效捕捉版图内的局部与全局空间依赖关系。模型基于我们生成的受物理启发的合成数据集进行训练，该数据集整合了关键物理因素，包括电源网格结构、单元密度分布和开关活动。模型性能通过均方误差和峰值信噪比等标准回归指标进行评估。实验结果表明，所提方法能以毫秒级推理时间准确预测IR压降分布，实现快速的签核前筛查与迭代设计优化。该框架旨在作为补充性的早期分析工具，为设计者在昂贵的签核分析前提供快速的IR压降洞察。实现代码、数据集生成脚本及交互式推理应用已公开于：https://github.com/riteshbhadana/IR-Drop-Predictor。实时应用可通过以下链接访问：https://ir-drop-predictor.streamlit.app/。