We propose a systematic framework to conduct design-technology pathfinding for power, performance, area, and cost (PPAC) in advanced nodes. Our goal is to provide a 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 Cheng et al. (2022), 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 to Kim et al. (2022). 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 evaluate PPAC across scaling boosters and artificial designs in a predictive technology node.

Machine learning-guided optical proximity correction, called ML-OPC in this paper, has recently been proposed to alleviate long runtime of model-based OPC. ML-OPC using regression methods has been presented but with limited prediction accuracy. We propose neural network classifier-based OPC (NNC-OPC), in which a neural network classifier serves as a mask bias model. A few techniques are applied to enhance basic NNC-OPC: parameterization of layout segments using polar Fourier transform signals, dimensionality reduction through weighted principal component analysis, and sampling of training layout segments. Training segments are typically imbalanced over the range of mask biases, which may cause large prediction error for segments that appear less frequently. This is resolved by three techniques: 1) synthetic data generation; 2) class reorganization; and 3) an adaptive learning rate. Experiments with NNC-OPC with all techniques applied indicate that prediction error of mask bias and training time are reduced by 29% and 80%, respectively, compared to state-of-the-art ML-OPC with regression methods.