Predictive Yield & Process Optimization Engineer

Samsung Electronics; Austin (TX); On-site; USD 95,000 - 120,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) matching

Job summary

Samsung Austin Semiconductor seeks a highly analytical Yield and Process Control and Investigation (PCI) Engineer to monitor wafer manufacturing, drive yield improvement, and investigate process excursions. You will use SPC, defect data, and parametric results to identify root causes and implement robust solutions for stability and high yield.

You will work with Process, Equipment, and Integration teams, applying advanced data science and ML with Python, JMP, and SQL to build automated analytics

Qualifications

• Bachelor of Engineering/Master of Science in Electrical Engineering, Materials Science, Physics, Applied Physics, or related field
• Understanding semiconductor device physics and CMOS fabrication processes
• Understanding electrical parametric testing and defect inspection mechanisms
• Strong technical writing and presentation skills for diverse audiences

Responsibilities

• Predictive Yield Modeling: develop, implement, validate yield prediction models
• Baseline Forecasting: forecast yield trajectories for ramping and mature nodes
• Yield Analysis & Improvement: perform data mining and statistics to identify yield limiters
• Root Cause & Failure Analysis: lead RCA correlating electrical signatures with defects
• Data Science & Modeling: apply machine learning to yield models and analytics pipelines
• Yield Management: define KPIs and improvement strategies; mentor engineers

Skills

Semiconductor data analysis

Python

JMP

SQL

Root cause analysis

Cross-functional collaboration

Technical writing

Education

Electrical Engineering / Materials Science / Physics

Tools

Python

JMP

SQL

Job description

Samsung Austin Semiconductor seeks a highly analytical Yield and Process Control and Investigation (PCI) Engineer to monitor wafer manufacturing, drive yield improvement, and investigate process excursions. You will use SPC, defect data, and parametric results to identify root causes and implement robust solutions for stability and high yield.

You will work with Process, Equipment, and Integration teams, applying advanced data science and ML with Python, JMP, and SQL to build automated analytics