Degree Requirement

A student will be required to maintain a minimum GPA of 3.0 in each semester and is subject to the general MSc programme rules on continuation/graduation/dismissal of candidature. The student is expected to graduate after obtaining 40 units and achieving a GPA of at least 3.0 within the maximum candidature.

A student is expected to:

• fulfill a minimum of 10 passed courses that is 40 Units including the required number of core and elective courses.
• achieve a minimum Grade Point Average (GPA) of at least 3.0 to graduate, and
• fulfill required e-courses as and when imposed at University level.
• complete requirement within maximum candidature.

Students should note that the final composition of courses taken is subject to approval, as it must always reflect a Degree in Semiconductor Technology and Operations.

Not all listed graduate courses will be offered in every semester or academic year. The course offering is subject to changes due to the availability and schedules of teaching staff. Students should note this when planning their study programme.

It is the student’s responsibility to ensure that he/she meets the pre-requisite requirements (if any) for the courses concerned.

Upon successful completion of the degree requirement, the student will be awarded the degree in Master of Science (Semiconductor Technology and Operations).

Workload

Typically, the academic study workload in a semester are as follows:

General Track

To be awarded the MSc in Semiconductor Technology and Operations, the student must successfully complete a programme of study consisting of at least 40 Units with a minimum GPA of at least 3.00 (average grade of B-), which comprise of the following:

• 2 core courses (any 4 Units each from Technology and Operations lists);
• 4 elective courses (any 8 Units each from Technology and Operations lists);
• Remaining units from optional (either Project OR Internship component only), and/or from the basket of core courses and/or required elective course lists.
• Fulfill compulsory e-courses as and when imposed at University level.

Section	Semiconductor Technology	Operations
Core (fulfil at least 8 Units, 1 from each section)	EEK5101 Integrated Circuit Technology and Design Methodology EEK5102 Heterogeneous Integration, IC Packaging and Testing	EEK5301 Evolution and Transformation of Semiconductor Industry EEK5302 Semiconductor Supply Chain and Demand Management
Required Elective (fulfil at least 16 Units, 2 from each section)	EEK5103 Photonic Integrated Circuits EEK5104 Bioelectronics EEK5105 Semiconductor Materials and Devices EEK5106 Semiconductor Yield and Failure Analysis EEK5107 SiPh Product, Test Characterization & Yield Eng EEK5108 Silicon Microsystem Design and Fabrication MLE5230 Material Characterisation Methodology IE5231 Statistical Methods for Process Design and Control IE5123 Reliability Engineering	MT5001/G Intellectual Property Management and Innovation Strategy IE5107 Operations Analysis and Management IE5203 Decision Analysis IE5121 Quality Planning and Management MT5922 Concepts and Applications of Engineering Leadership
Remaining Elective (16 Units)	Optional (either Project OR Internship component only): EEK5001 or EEK5003 Semiconductor Technology and Operations Project I or II, respectively (4 or 8 Units, respectively); OR EEK5888 Semiconductor Technology and Operations Internship (8 Units); and/OR
	Courses within MSc (STO) programme (Refer to above basket of Core and Elective course lists) Up to a maximum of two relevant elective courses (4 units per course) from the MSc EE and CEG programme baskets. Eligible courses are limited to EE5XXX or CEG5XXX, excluding service courses, and will be subject to approval on a case-by-case basis.

Each course is equal to 4 Units unless otherwise stated.

EEK5001, EEK5003 Project Course: Semiconductor Technology and Operations Project I & II (optional)

Student may opt to work on a MSc project which can be taken in lieu of 4 or 8 Units course and have it count towards the degree requirement.

This is an optional project component. Note that this is NOT a thesis nor dissertation component.

There are 2 types of projects (only ONE of these types can be taken):

The projects may be proposed by the faculty members or by the students.

Current registered students may view the list of project proposal from ECE Faculty member at the ECE student’s e-station.

For self-proposed project, student will need to find an appropriate ECE faculty member as a supervisor. The student experience and expertise in his/her workplace or area of interest, should help to identify a suitable project.

Visit the ECE website to browse through information on the research areas of our ECE Faculty members.

EEK5888: Semiconductor Technology and Operations Internship (optional)

Student will be placed in a Singapore-based organisation for 15-weeks working on core and emerging topics related to Semiconductor Technology and Operations (e.g. new product development, technology intelligence and forecasting, product technology road mapping, technology strategy, intellectual property management and business models).

Students will be jointly supervised by a team comprising of NUS academic staff and the Company’s appointed manager. Assessments will be done periodically, leading to a project report and presentation at the end of the attachment.

This option is eligible for full time student only.

Note:

• Students taking this internship course should not be holding full-time employment.
• This internship course will commence from approximately from the vacation period and into the second semester of study.
• Student taking this internship course are strongly advise to take note of the maximum allowed workload in a semester.

Industry Scholarship Specialisation

Applied AI for Materials and Process Engineering Specialisation (name under revision)

The Applied AI for Materials and Process Engineering specialisation aims to develop engineering professionals with expertise at the intersection of semiconductor technology, advanced materials, and artificial intelligence. The curriculum integrates semiconductor manufacturing and operations with emerging AI methodologies, enabling students to address complex industrial challenges through data-driven and computational approaches. Areas of application include process optimisation, predictive analytics, yield improvement, and intelligent manufacturing systems. Through a combination of advanced coursework and applied project work, students will develop the interdisciplinary skills required to drive innovation in AI-enabled semiconductor manufacturing, materials engineering, and intelligent industrial systems.

Note: Admission to this specialisation is by selection and requires a relevant compulsory project or internship as part of the specialisation requirements.

 

Section	Semiconductor Technology	Operations
Core (fulfil 8 Units, 1 from each section)	EEK5101 Integrated Circuit Technology and Design Methodology EEK5102 Heterogeneous Integration, IC Packaging and Testing	EEK5301 Evolution and Transformation of Semiconductor Industry EEK5302 Semiconductor Supply Chain and Demand Management
Required Elective (fulfil 8 Units, 1 from each section)	EEK5103 Photonic Integrated Circuits EEK5104 Bioelectronics EEK5105 Semiconductor Materials and Devices EEK5106 Semiconductor Yield and Failure Analysis EEK5107 SiPh Product, Test Characterization & Yield Eng EEK5108 Silicon Microsystem Design and Fabrication MLE5230 Material Characterisation Methodology IE5231 Statistical Methods for Process Design and Control IE5123 Reliability Engineering	MT5001/G Intellectual Property Management and Innovation Strategy IE5107 Operations Analysis and Management IE5203 Decision Analysis IE5121 Quality Planning and Management MT5922 Concepts and Applications of Engineering Leadership
Required Elective (Applied AI for Materials and Process Engineering) (Fulfil 16 units)	EE5442 Memory Technologies and Their Emerging Applications EE5907 Pattern Recognition CEG5304 Deep Learning for Digitalisation Technologies CEG5305 Introduction to Generative AI with Foundation Models MLE5242 Advanced Methods in AI for Materials MLE5219 Materials Informatics: The Role of Big Data MLE5217 Foundations of Machine Learning for Materials Science MLE5218 Materials Discovery with AI MLE5215 Atomistic Modelling of Molecules and Materials MLE5210 Modelling and Simulation of Materials MLE5243 Current Topics in Materials AI
Compulsory Project OR Internship (8 Units)	EEK5003 Semiconductor Technology and Operations Project II (8 Units); OR EEK5888 Semiconductor Technology and Operations Internship (8 Units)

Each course is equal to 4 Units unless otherwise stated.

Compulsory Project or Internship for Specialisation Track

Students pursuing the specialisation are required to complete either a single 8-unit project or an approved 8-unit internship module under the MSc (Semiconductor Technology and Operations) programme that is aligned with the specialisation focus.

The project provides students with an opportunity to apply the fundamental knowledge and practical skills acquired through the coursework component of the specialisation to real-world engineering challenges. Projects may be undertaken in collaboration with industry partners and under the supervision of NUS faculty members, providing students with opportunities to address real-world challenges while applying state-of-the-art techniques and methodologies. Through the project, students will integrate knowledge acquired through coursework and develop analytical, problem-solving, innovation, and communication skills relevant to semiconductor technology, materials and process engineering, and artificial intelligence.

Students undertaking internships will be attached to their sponsoring companies, where they will gain first-hand experience in industrial environments, apply their technical knowledge to practical engineering challenges, and develop a deeper understanding of semiconductor manufacturing, engineering operations, and industry practices.

Through the project or internship, students will strengthen their technical and professional competencies while gaining practical experience relevant to the semiconductor industry.