REDM: Regression-Guided Diffusion Modeling for Universal Soft Sensor Enhancement in Semiconductor Process Control

In semiconductor manufacturing, soft sensors play a key role in Advanced Process Control (APC) by enabling realtime wafer-to-wafer monitoring. However, their performance is often limited by sparse labeled data, process variability, and model-specific tuning. To address these challenges, we propose REDM: a Regression-Guided Diffusion Modeling framework designed to boost the accuracy and robustness of soft sensor prediction across diverse fabrication stages. REDM generates highfidelity virtual data guided by predictive regression objectives and incorporates a quality-aware filtering mechanism based on Sliced Wasserstein Distance and intra-subset Cosine Similarity. Through multi-objective selection techniques, REDM identifies informative virtual samples that balance distributional similarity and internal diversity, thereby enhancing downstream model training. We evaluate REDM on real-world datasets from three major semiconductor process stages: Chemical Vapor Deposition (CVD), Etching, and Chemical Mechanical Polishing (CMP). Across various regression models, REDM consistently enhances soft sensor performance, with an average $\mathbf{R}^{2}$ improvement of $3.27 \%$. Its independence from process-specific customization makes REDM a scalable and process-aware solution for soft sensor enhancement in smart manufacturing.