  We propose a systematic framework to conduct design-technology pathfinding
for PPAC in advanced nodes. Our goal is to provide configurable, scalable
generation of process design kit (PDK) and standard-cell library, spanning key
scaling boosters (backside PDN and buried power rail), to explore PPAC across
given technology and design parameters. We build on PROBE2.0, which addressed
only area and cost (AC), to include power and performance (PP) evaluations
through automated generation of full design enablements. We also improve the
use of artificial designs in the PPAC assessment of technology and design
configurations. We generate more realistic artificial designs by applying a
machine learning-based parameter tuning flow. We further employ
clustering-based cell width-regularized placements at the core of routability
assessment, enabling more realistic placement utilization and improved
experimental efficiency. We demonstrate PPAC evaluation across scaling boosters
and artificial designs in a predictive technology node.
