The main research issue in semiconductor etching revolves around the integration of AI/ML and operations research to optimize the etching process. This involves two core challenges: precise end-point prediction and parameter optimization. AI/ML algorithms are utilized to analyze sensor data, spectroscopy, and real-time imaging for accurate end point predictions, ensuring etching stops at the exact depth and profile required. This approach significantly improves upon traditional methods by providing a more reliable and precise prediction mechanism. Simultaneously, operations research techniques are applied to fine-tune etching parameters, including gas flow, pressure, power, and duration, aiming to enhance process efficiency, consistency, yield, and reduce defects and costs. The integration of predictive analytics with parameter optimization represents a significant advancement in semiconductor manufacturing, offering a pathway to more efficient, precise, and cost-effective etching processes.