Job Description
The Electrical and Computer Engineering (ECE) Department of National University of Singapore (NUS) is in search of candidate for a research fellow position to support 3D-IC package fault localisation and failure analysis (FA) techniques with Artificial Intelligence (AI)/Machine Learning (ML)/Deep Learning (DL) capability.
You will work closely with our researchers, students, and professors, including Co-Investigators from A*STAR: I2R and IMRE, and several industry collaborators to understand and derive insights from scientific experiments and data.
The successful candidate will be assisting in the research to enable a variation-tolerant and technology-agnostic ML guided system to achieve 10x or greater reduction in time taken for FA process; reducing from weeks/months of fault identification to hours. This in turn allows problems to be identified and resolved quickly leading to faster time-to-market, yield and productivity improvements.
Responsibilities will involve:
- Develop magnetic field imaging (MFI) workflow and perform correlative study with Lock-in Thermography (LIT).
- Develop ML algorithms to guide MFI data processing.
- Develop ML algorithms to capture X-ray image of 3D-IC defects.
- Develop ML algorithms to extract features and identify defects.
- Develop 3D-IC forward predictive models (i.e. include chip and circuit blocks) and ML feature set to integrate with MFI tool interfaces.
- Lead research article publications and invention disclosures.
- Lead new equipment purchases and commissioning.
- Assume super-user role of multiple equipment (ownership and maintenance) and leadership role for safety.
- Provide trainings and certifications to new users.
(Interested applicants should send their curriculum vitae via email to [email protected] ).
Qualifications
- PhD in Electrical, Electronics, Mechanical, Chemical Engineering, Physics, Material Science or its equivalent from a reputable University/Institute is preferred, or equivalent related experience.
- Candidate with experience in microelectronic research, characterisation, metrology, electrical testing, FA and strong in data analysis will have advantage. Knowledge of AI/ML/DL development and TCAD modelling.
- Strong analytical and problem-solving skills.
- Motivated and possesses effective communication skills including writing and presenting.
- Ability to work independently and collaborate within a diverse team.
- Open to Fixed Term Contract
More Information
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Electrical and Computer Engineering
Employee Referral Eligible: No
Job requisition ID : 23132
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