This is an individual contribution role as a Plating Process Control Engineer in manufacturing engineering operations responsible for monitoring and controlling plating processes to ensure quality and yield targets while implementing process improvements and optimization initiatives. The role encompasses managing plating bath chemistry and refresh schedules, troubleshooting process deviations, conducting statistical analysis to support process changes, and ensuring compliance with contamination control procedures. The engineer is also expected to leverage data analytics and machine learning techniques to establish linkages between Key Process Input Variables (KPIVs) and Key Process Output Variables (KPOVs), enabling predictive process control and data-driven decision making. Key technical competencies include understanding electroplating principles and chemistry, knowledge of semiconductor manufacturing processes, experience with process control systems and data analysis, and familiarity with cleanroom operations and safety protocols. The engineer also participates in FMEA activities, risk assessments, and supports change control processes such as PCN and TECN to maintain operational excellence in plating operations.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
- Focal person for plating facilities related issues, ie plating DI water trend monitoring, LPC and
- contamination
- Focal person of plating chemical planning and inventory
- Focal person for plating pretreatment, strip line, filter change and descaling.
- Monitor and control plating processes to ensure quality and yield targets
- Implement process improvements and optimization initiatives
- Troubleshoot process deviations and implement corrective actions, which include thickness control, anodic protection
- Troubleshoot line to line variation and implement corrective actions
- Ensure compliance with contamination control procedures
- Participating in FMEA activities and risk assessments
- Monitor and improve plating OEE performance
- Implement process optimizations to reduce speed losses and improve efficiency
- Track and analyze OEE metrics to identify improvement opportunities
- Support implementation of process parameter revisions to enhance OEE
- Identify, define, and document KPIVs and KPOVs for plating processes and establish data-driven linkages between them
- Develop and maintain process monitoring dashboards using data analytics tools to visualize KPIV-KPOV relationships
- Apply machine learning models (e.g., regression, classification, anomaly detection) to predict process outcomes and enable proactive process control
- Collaborate with data engineers or IT teams to integrate manufacturing data from equipment and sensors into analytics platforms
- Utilize statistical process control (SPC) and advanced analytics to detect processes that drift and trigger timely corrective actions
