About the role
AI summarisedPrincipal/Staff Engineer for PCVD film deposition process development in semiconductor technology development. The role involves developing advanced thin film deposition processes (ALD, LPCVD, PECVD, PVD) for next-generation memory, designing experiments, performing root-cause analysis, and collaborating with cross-functional teams. Requires deep expertise in deposition techniques, characterization, and data analytics.
IDMFull-timeSTPG
Key Responsibilities
- Develop and optimize advanced thin film deposition processes (ALD, LPCVD, PECVD, PVD) for cutting-edge device architectures
- Design and execute complex DOE experiments to improve process capability, uniformity, and reliability
- Perform in-depth root-cause analysis using advanced modeling and characterization techniques
- Collaborate with integration, other process areas, metrology, and equipment engineering teams to solve critical technical challenges
- Drive innovation in film deposition for improved performance, cost efficiency, and sustainability
- Utilize advanced data analytics and modeling to predict process behavior and accelerate development cycles
- Define technology roadmaps and lead strategic research initiatives. Generate intellectual property and influence future deposition technology directions
- Mentor junior engineers and lead multi-disciplinary technical projects
Requirements
- Ph.D/Master's in Chemical Engineering, Materials Science, Electrical Engineering, Physics, or related field
- Master with minimum 8 years or PhD with minimum 5 years hands-on experience in advanced thin film deposition, characterization or related semiconductor field
- Expertise in ALD, LPCVD, PECVD, and PVD for advanced nodes
- Strong understanding of film growth mechanisms, surface chemistry, and plasma physics
- Knowledge of thermodynamics, chemical kinetics, and reaction engineering
- Proficiency in DOE, SPC, and advanced statistical analysis
- Some familiarity with TCAD or process simulation tools for film modeling
- Data analytics and machine learning for process optimization
- Hands-on experience with TEM, SEM, XPS, SIMS, XRD, AFM, ellipsometry, and other thin film metrology
- Advanced root-cause analysis and failure mode identification
- Ability to develop predictive models for process behavior