This paper presents an innovative approach to 3D mixed-size placement in heterogeneous face-to-face (F2F) bonded 3D ICs. We propose an analytical framework that utilizes a dedicated density model and a bistratal wirelength model, effectively handling macros and standard cells in a 3D solution space. A novel 3D preconditioner is developed to resolve the topological and physical gap between macros and standard cells. Additionally, we propose a mixed-integer linear programming (MILP) formulation for macro rotation to optimize wirelength. Our framework is implemented with full-scale GPU acceleration, leveraging an adaptive 3D density accumulation algorithm and an incremental wirelength gradient algorithm. Experimental results on ICCAD 2023 contest benchmarks demonstrate that our framework can achieve 5.9% quality score improvement compared to the first-place winner with 4.0x runtime speedup.

中文翻译：

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使用宏进行分析异构芯片到芯片 3D 放置

本文提出了一种在异构面对面 (F2F) 粘合 3D IC 中进行 3D 混合尺寸贴装的创新方法。我们提出了一个分析框架，该框架利用专用密度模型和双层线长模型，有效地处理 3D 解决方案空间中的宏和标准单元。开发了一种新颖的 3D 预处理器来解决宏和标准单元之间的拓扑和物理差距。此外，我们提出了一种用于宏旋转的混合整数线性规划（MILP）公式，以优化线长。我们的框架是通过全面的 GPU 加速实现的，利用自适应 3D 密度累积算法和增量线长梯度算法。ICCAD 2023 竞赛基准的实验结果表明，与第一名获胜者相比，我们的框架可以实现 5.9% 的质量得分提高，运行时加速提高 4.0 倍。