Nokia
Nokia

Principal Manufacturing Engineer

RoleManufacturing
LevelPrincipal
LocationUnited States
WorkOn-site
TypeFull-time
Posted1 month ago
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About the role

The Principal Process Integration Engineer is responsible for ensuring the quality, reliability, and yield of InP-based Photonic Integrated Circuit (PIC) products. This role plays a key part in ramping high-volume production and works cross-functionally to troubleshoot and optimize wafer fabrication processes, resolve yield issues, and support next-generation PIC platform development.

  • Ph.D. in Electrical Engineering, Materials Science, Physics, Chemical Engineering, or a related field
  • 8+ years of experience in semiconductor wafer fabrication
  • Hands-on experience with III-V processes, including epitaxy, wet and dry etching, photolithography, and metal deposition
  • Strong understanding of semiconductor processes and device physics
  • Proven analytical and problem-solving skills with proficiency in data analysis tools (e.g., JMP, Python)
  • Experience with SPC, DOE, and yield optimization methodologies
  • Excellent communication skills with the ability to present technical work to diverse stakeholders
  • Own wafer quality and yield using deep knowledge of fab processes, device physics, and test/reliability data
  • Analyze process deviations and test failures to identify root causes and implement corrective actions
  • Establish, maintain, and monitor inline fab process specifications and capability (CPK)
  • Drive yield improvement initiatives from concept through qualification and production release
  • Define design rules and develop test structures for effective process control
  • Support technology development, transfer, and manufacturing ramp for next-generation PIC platforms
  • Partner with Process Engineering to improve process stability and capability
  • Collaborate with Operations to improve throughput and reduce cycle time

Required skills

Semiconductor fabrication

Process integration

Yield optimization

Device physics

SPC

DOE

Root cause analysis

Data analysis

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