Yield Engineer, RF Final Test Integration (Starlink/Akoustis)

RESPONSIBILITIES:

• Develop and implement RF test cases and calibration procedures for BAW filter devices operating in the 2 GHz to >10 GHz range
• Establish strong linkages between wafer probe data and final RF test performance, driving root cause analysis of yield detractors
• Use statistical tools such as SPC and DOE to monitor, control, and improve yield across the manufacturing flow
• Develop and maintain Cost of Ownership (CoO) and risk models to evaluate probe test strategies and ensure effective test coverage
• Sustain and optimize workflow and throughput in a high-mix RF test work center, driving continuous improvements in cycle time and cost
• Collaborate with design teams to provide feedback on DFM (Design for Manufacturability) and yield optimization opportunities
• Lead and mentor test operators, ensuring proper execution of test plans and maintenance of clean, organized workflows
• Support installation and qualification of new test tools, including both capability enhancements and capacity expansions
• Work closely with operation team and tool vendors to resolve equipment and process-related issues
• Create and maintain Standard Operating Procedures (SOPs), engineering instructions, and test documentation
• BASIC QUALIFICATIONS:
• Bachelor’s degree in electrical engineering, physics, materials science, or other STEM discipline
• 1+ years of experience in RF testing, yield engineering, or semiconductor manufacturing(internship or professional)
• PREFERRED QUALIFICATIONS:
• 2+ years of experience in RF test development or semiconductor yield engineering
• Working knowledge of RF measurement principles, including S-parameters, insertion loss, return loss, and calibration
• Familiarity with RF BAW filter technology and associated test challenges
• Experience using automated probe stations, VNAs, and RF measurement tools
• Strong analytical skills with demonstrated ability to perform data analysis, root cause analysis, and yield correlation
• Proficiency in Statistical Process Control (SPC), DOE, and related quality improvement methodologies