Stronger Mixed-Size Placement Backbone Considering Second-Order Information

Macro placement is a critical step in modern very large-scale Integration (VLSI) physical design. Placing macros with varying sizes significantly impacts the eventual quality of results. Many studies attempt to improve macro placement solutions leveraging existing analytical placement algorithms as the backbone. However, existing analytical placement algorithms may fail to converge for mixed-size designs if the parameters are not well-tuned. In this work, we propose a stronger mixed-size placement backbone with robust global placement convergence and macro legalization. Experimental results show that our method outperforms state-of-the-art works with better solution quality and fewer optimization iterations on various benchmarks including MMS, ISPD2005, and TILOS.