Job Description

Offer 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: * Contribute to magnetic field imaging (MFI) system and 3D solver solutions optimisation.

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

(Interested applicants should send their curriculum vitae via email to ).

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

Requirements

Additional Information

Where to apply Website

STATUS: EXPIRED

EURAXESS