Three-dimensional integration and advanced packaging, including monolithic 3D ICs, 2.5D chiplet-based integration, and emerging 5.5D integration with glass interposers, have demonstrated strong potential to sustain system scaling beyond the limits of monolithic design. However, in contrast to the well-established RTL-to-GDS implementation flows for conventional 2D IC design, the lack of standardized implementation flows across these integration paradigms remains a fundamental bottleneck. The absence of unified implementation flows raises a high barrier to both the development and evaluation of new methodologies for 3D IC design. Researchers often rely on ad hoc assumptions, incomplete toolchains, or simplified design enablements, making it difficult to develop solutions that generalize across different integration paradigms and implementation scenarios. Moreover, inconsistent evaluation settings hinder the interpretation of results across different works, limiting the ability to understand design trade-offs and to accumulate progress toward a coherent methodology. This special session presents four complementary efforts that collectively address this challenge by advancing implementation methodologies across diverse 3D integration paradigms: open-source implementation platforms for advanced packaging and 3D ICs; a commercial-grade full-chip methodology for face-to-face 3D ICs; AI-driven multi-physics co-optimization for glass-interposer-based 5.5D systems; and multi-domain design optimization of thermally limited 3D ICs for AI and HPC systems. Together, these works highlight a clear shift from paradigm-specific solutions toward flow-level and cross-domain co-optimization, motivating the development of unified, scalable, and interpretable implementation frameworks for next-generation 3D ICs and advanced packaging. By bringing together representative efforts under shared contexts, the session provides a coherent perspective on the evolving 3D IC design space, helps clarify key challenges and research directions, and facilitates more consistent interpretation of results across integration paradigms.
