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.

翻译：本文提出了一种面向异构面对面键合三维集成电路中混合尺寸布局的创新方法。我们提出了一种解析框架，该框架利用专用密度模型和双层线长模型，在三维解空间中有效处理宏模块与标准单元。为解决宏模块与标准单元之间的拓扑与物理差异，我们开发了一种新颖的三维预处理器。此外，我们提出了用于宏模块旋转的混合整数线性规划（MILP）公式，以优化线长。我们的框架通过全尺度GPU加速实现，采用自适应三维密度累积算法和增量式线长梯度算法。在ICCAD 2023竞赛基准测试上的实验结果表明，与第一名优胜者相比，我们的框架在质量得分上提升了5.9%，同时实现了4.0倍的运行速度提升。