Research Fellow (3D-IC Package Fault Localisation and Failure Analysis (FA))

Job Description

The Electrical and Computer Engineering (ECE) Department of National University of Singapore (NUS) is looking for applicants for research fellow positions 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 candidates will be supporting 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 include:

1. Contribute to magnetic field imaging (MFI) system and 3D solver solutions optimisation.
2. Involve in 3D-IC forward predictive modelling (i.e. include chip and circuit blocks) and ML feature set to integrate with MFI tool interfaces.
3. Support and aid researchers in data generations, acquisitions and analytics required for ML MFI fault isolation.
4. Contribute to research article publications and invention disclosures.
5. Assist new equipment purchases and commissioning.
6. Assume super-user role of single equipment (ownership and maintenance).
7. Provide trainings and certifications to new users.

(Interested applicants should send their curriculum vitae via email to eleykl@nus.edu.sg).

Qualifications

• PhD Degree 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. 
• Self-driven and good communication skill including writing and presenting.
• 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: N
Job requisition ID : 19972