Job DescriptionPrimary Duties & Responsibilities- Identify, prioritize, and collaborate with cross-functional engineering teams on AI/ML, Factory Automation, APC, and FDC projects that deliver the highest operational impact and return on investment (ROI) across the wafer fabrication facility.
- Develop, deploy, maintain, and enhance AI/ML applications, Factory Automation solutions, Advanced Process Control (APC) controllers, and Fault Detection & Classification (FDC) models to improve yield, process stability, equipment performance, throughput, and overall manufacturing efficiency.
- Collect, integrate, and analyze manufacturing data generated through Factory Automation, APC, FDC, MES, process tools, metrology systems, equipment sensors, and manufacturing databases.
- Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines and data engineering workflows to acquire, cleanse, transform, and integrate manufacturing data for advanced analytics, machine learning, and intelligent manufacturing applications.
- Develop machine learning models and advanced analytics solutions for yield prediction, process optimization, anomaly detection, predictive maintenance, equipment health monitoring, process drift detection, root cause analysis, and intelligent process control.
- Design and implement predictive analytics, dashboards, and decision-support applications that enable proactive identification of manufacturing issues and data-driven decision making.
- Translate process engineering requirements and customer requests into integrated AI/ML, automation, APC, and FDC solutions for specific process tools, process areas, and fab-wide optimization initiatives.
- Support semiconductor equipment integration using SECS/GEM communication standards, Manufacturing Execution Systems (MES), and factory automation infrastructure.
- Support new equipment installations, product introductions, and process technology transfers by implementing robust automation, APC/FDC, and AI/ML solutions.
- Troubleshoot and resolve automation, APC, FDC, data integration, database, and equipment communication issues to ensure reliable manufacturing operations.
- Collaborate with Process, Equipment, Manufacturing, Yield Enhancement, and IT teams to deploy intelligent manufacturing solutions and continuously improve yield, throughput, cycle time, equipment effectiveness (OEE), and engineering productivity.
Education & Experience- Minimum 5 years' experience in data analytics in semiconductor, materials, or a related industry; or demonstratable equivalent abilities.
- BS/MS or equivalent degree in computer science, software engineering, physics, mathematics, statistics or similar STEM field.
Skills- Leadership capabilities to independently lead complex technical initiatives, provide technical direction in AVI, AI/ML, and automation efforts, and influence cross-functional teams without direct authority.
- Strong interpersonal, collaboration, and problem-solving skills, with the ability to work effectively in high-pressure manufacturing environments.
- Experience modeling, analyzing, and validating complex, imperfect real-world manufacturing and inspection datasets, including image and in-situ equipment data.
- Strong understanding of statistical fundamentals and their application to machine learning, defect detection, process monitoring, and anomaly identification.
- Solid knowledge of semiconductor manufacturing processes; background in process engineering, materials science, or related natural sciences is a plus.
Working ConditionsPhysical Requirements- Ability to sustainably work on a computer full-time.
- Willingness and ability to work in a cleanroom environment as needed for system integration, testing, or troubleshooting activities.
Safety RequirementsAll employees are required to follow the site EHS procedures and Coherent Corp. Corporate EHS standards.
Quality and Environmental ResponsibilitiesDepending on location, this position may be responsible for the execution and maintenance of the ISO 9000, 9001, 14001 and/or other applicable standards that may apply to the relevant roles and responsibilities within the Quality Management System and Environmental Management System.
