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. Additional experiments on modern RISC-V designs further validate the generalizability and superiority of our framework.

翻译：本文提出了一种面向异质面对面（F2F）键合三维集成电路中混合尺寸三维布局的创新方法。我们提出了一种解析框架，该框架采用专用的密度模型和双层线长模型，能有效处理三维解空间中的宏模块与标准单元。为弥合宏模块与标准单元间的拓扑与物理间隙，我们开发了一种新颖的三维预处理器。此外，我们提出了一种基于混合整数线性规划（MILP）的宏模块旋转方案以优化线长。本框架通过自适应三维密度累积算法与增量式线长梯度算法，实现了全规模GPU加速。在ICCAD 2023竞赛基准测试上的实验结果表明，相较于原优胜方案，本框架可实现5.9%的质量分数提升，并取得4.0倍的运行速度提升。在现代RISC-V设计上的补充实验进一步验证了本框架的泛化能力与优越性。