ML-based Physical Design Parameter Optimization for 3D ICs: From Parameter Selection to Optimization

While various studies have shown effective parameter optimizations for specific designs, there is limited exploration of parameter optimization within the domain of 3D Integrated Circuits. We present the first comprehensive study, both qualitatively and quantitatively, comparing five state-of-the-art (SOTA) techniques for parameter optimization applied to 3D ICs. Additionally, we introduce an end-to-end machine learning-based framework, encompassing important parameter selection through optimization, all without human intervention. Extensive studies across six industrial designs under the TSMC 28nm technology node reveal that our proposed framework outperforms SOTA techniques in three different optimization objectives in both optimization quality and runtime.