  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.
