Principal/Staff Engineer, PCVD Film Deposition, TD

Job Summary

We are seeking a highly motivated engineer to join our Technology Development team focused on advanced thin film deposition processes for next-generation memory technologies. This role spans responsibilities from process development to technical leadership, depending on experience level (Principal/Staff).

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:

Education:

• Ph.D / Master's in Chemical Engineering, Materials Science, Electrical Engineering, Physics, or related field

Experience:

• Master with minimum 8 years or PhD with minimum 5 years hands-on experience in advanced thin film deposition, characterization or related semiconductor field

Skills:

Core Technical:

• 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

Analytical & Modeling:

• 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

Characterization:

• Hands-on experience with TEM, SEM, XPS, SIMS, XRD, AFM, ellipsometry, and other thin film metrology

Problem-Solving:

• Advanced root-cause analysis and failure mode identification

• Ability to develop predictive models for process behavior