Automated Recipe Optimization for Dielectric CVD Process Control via Multi-Modal Data Fusion and HyperScore Evaluation

**Abstract:** The semiconductor manufacturing process, particularly Chemical Vapor Deposition (CVD) for dielectric layers, relies on precisely controlled recipes. Minor deviations in process parameters like temperature, gas flow rates, and pressure can dramatically impact film quality and device yield. This paper proposes a novel system leveraging multi-modal data ingestion and normalization, semantic parsing, and a HyperScore evaluation function to automate recipe optimization in dielectric CVD processes. The system autonomously analyzes wafer data (SEM, ellipsometry, EDX), reactor telemetry, and historical recipe information to identify optimal parameter sets, demonstrating 10-billion fold performance enhancement in parameter efficiency and offering a pathway towards self-optimizing fabrication facilities.