Key Responsibilities:
- Implement and maintain fab-wide applications such as Manufacturing Execution Systems (MES), Advanced Process Control (APC), and Equipment Automation.
- Develop and maintain automation solutions for semiconductor manufacturing equipment (e.g., SECS/GEM, EDA, OPC, FDC).
- Develop real-time monitoring and anomaly detection systems using machine learning.
- Work with process and yield engineers to create predictive models for defect reduction and process optimization.
- Develop and deploy AI/ML models to optimize semiconductor manufacturing processes, reduce downtime, and improve yield.
- Implement AI-powered predictive maintenance for equipment health monitoring and failure prevention.
- Work with engineering and data science teams to integrate AI-driven analytics into factory systems.
- Enhance process automation with AI-based decision-making algorithms and real-time analytics.
- Implement and optimize data acquisition, analysis, and predictive maintenance tools for production systems.
- Work with equipment suppliers and internal teams to integrate new equipment into fab IT infrastructure.
- Ensure seamless connectivity between manufacturing tools and factory control systems.
Qualifications & Skills:
Education:Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Information Technology, Electrical Engineering, or a related field.
Experience:5+ years in semiconductor manufacturing IT, automation, AI, or a similar role preferred.
Technical Skills:
- Knowledge of MES, SECS/GEM, EDA, APC, and factory automation systems.
- Knowledge in Digital twins, smart manufacturing, cyber-physical systems.
- Knowledge in Integration of AI-driven automation with robotic process automation (RPA).
- Experience in Industrial IoT (IoT) and real-time sensor data acquisition.
- Edge computing and real-time analytics for semiconductor fab operations
- Experience with AI/ML frameworks, Expertise in programming (Python, R, Java, SQL) for AI model development and data analytics.
- Familiarity with cloud computing (AWS, Azure, GCP) and edge computing for real-time AI applications.
- Understanding of network infrastructure, cybersecurity, and IT system administration.
