Quality and Reliability Engineer, Modeling and Data Analysis, Google Cloud
Onsite
United StatesTel Aviv, IsraelHaifa, Israel
United States
Full Time Posted a month ago
Job Type
Full Time
Job Details
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Tel Aviv, Israel; Haifa, Israel. Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- Candidates will typically have 3 years of experience with industry-standard tools, languages and methodologies relevant to the development of silicon-based ICs and chips.
- Experience with CMOS technology and device physics.
- Experience in reliability modeling, data analytics, and statistics.
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- Data Analytics experience and ability to identify commonalities and abnormalities.
- Knowledge of semiconductor device physics, failure mechanisms, and accelerated test methodologies.
- Knowledge of JEDEC and other industry-standard reliability specifications. Working knowledge of Design-for-Reliability guidelines and implementation techniques.
- Excellent communication and collaboration skills.
- Develop and implement physics-based statistical Quality and Reliability models (ELF, TDDB, NBTI, HCI, Time zero failures, etc.) to predict silicon device failure mechanisms, degradation patterns, and lifetime behaviors.
- Collaborate with cross-functional teams to develop product Design-for-Reliability resilience based on product mission profile, technology node guidelines, modeling and analysis findings (e.g., SER, EMIR, PERC, HVDRC, Aging, etc. ).
- Extract, manipulate, and analyze large volumes of data from Silicon and Package qualification programs (e.g., HTOL, ELFR, ESD, LU, UHAST, TCT, etc. ), High Volume MFG, and field returns to identify failure mechanisms, reliability trends, and opportunities for yield and quality and reliability improvement.
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