Heterogeneous integration (HI) has emerged as a potential system-level scaling solution to meet the exponential growth in artificial intelligence (AI) applications. However, to fully leverage the advantages of HI systems, several critical challenges remain. In this work, we address two major challenges faced by emerging 3-D heterogeneous integration (3-D HI) architectures: (1) difficulty of scaling interconnect technologies and (2) the impact of 3-D stacking on the power delivery network (PDN). First, we propose and demonstrate the use of selective Cobalt atomic layer deposition (Co ALD) for high-density die-to-wafer (D2W) bonding on a 2-D lateral gap and 3-D gap testbed. Second, we demonstrate inverse hybrid bonding (IHB) on a 2.5-D and 3.5-D multi-chip testbed and measure the electrical performance and reliability to assess process feasibility. Finally, we present a MATLAB-based PDN framework for emerging 3-D heterogeneous integration architectures and perform a design space exploration for early predictive modeling and finding the limitations of emerging HI architectures. The framework is also used to design an integrated methodology for early PDN design space exploration for 3-D compute-in-memory (CIM) architectures to help bridge interconnect design and CIM inference accuracy for rapid prototyping.