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

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:

1.    Develop magnetic field imaging (MFI) workflow and perform correlative study with Lock-in Thermography (LIT).
2.    Develop ML algorithms to guide MFI data processing.
3.    Develop ML algorithms to capture X-ray image of 3D-IC defects.
4.    Develop ML algorithms to extract features and identify defects.
5.    Develop 3D-IC forward predictive models (i.e. include chip and circuit blocks) and ML feature set to integrate with MFI tool interfaces.
6.    Lead research article publications and invention disclosures.
7.    Lead new equipment purchases and commissioning.
8.    Assume super-user role of multiple equipment (ownership and maintenance) and leadership role for safety.
9.    Provide trainings and certifications to new users.

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

•    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

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : Electrical and Computer Engineering

Employee Referral Eligible: No

Job requisition ID : 23132