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The MSc (Electronics) programme is offered on a part-time and full-time basis for engineers in the electronics industry who would like to have graduate training in various topics.
integrated circuit design, microelectronics fabrication and manufacture of electronic and photonic products
Have a good relevant bachelor's degree
Relevant working experience is an advantage
For applicants whose native language is not English, TOEFL/IELTS score is to be submitted with the application for admission:
TOEFL Score (Test dates must be within 2 years or less from the date of application):
≥ 563 (paper-based)
≥ 223 (computer-based)
≥ 85 (internet-based)
IELTS Score (Test date must be within 2 years or less from the date of application):
≥ 6.0
Applicants without TOEFL/IELTS would still be eligible to apply, but they may be subjected to an interview/test if deemed necessary by the School.
Programme Structure
There are two options of study, one with coursework only, and the other with coursework and dissertation. Each course is of 3 AUs and consists of 39 hours of lectures. Candidates who undertake a project of 6 AUs must submit a dissertation on it. Students are encouraged to choose the dissertation option only if they have a strong interest in pursuing further research studies.
Option 1 | Option 2 |
| 10 courses (30 AUs in total) | 8 courses + dissertation project (30 AUs in total) |
| 4 specialized electives (≥ 12 AUs) | 4 specialized electives (≥ 12 AUs) |
| 6 general electives (≤ 18 AUs) | 4 general electives (≤ 12 AUs) |
| Dissertation (6 AUs) |
Note: The programme structure will be subject to change without prior notice.
Duration
Both full-time and part-time programmes are offered (unless stated). Part-time candidates are expected to obtain permission from their employer before admission to the programme. Most classes are conducted in the evenings, while examinations are conducted during office hours.
Type of Coursework Programme | Minimum Candidature | Maximum Candidature |
| Master of Science (Full-Time) | 1 year | 3 year |
| Master of Science (Part-Time) | 2 year | 4 year |
Programme Calendar
| Semester 1 | August to December |
| Semester 2 | January to May |
| Week 1 to 14 | Lecture (Inclusive of 1-week recess) |
| Week 15 to 17 | Examinations |
| Other | Vacation |
Graduate courses offered by Master of Science (MSc) Electronics:
Specialized Elective Courses (Students are required to take a min of 4 out of all the 6 specialized elective courses)
| Course Code | Course Title | Course Content | AUs |
| EE6306 | Digital Integrated Circuit Design | Review of Integrated Circuit Fundamentals. Layout and Design Issues. CMOS Digital Circuits. BiCMOS Digital Circuits. Sub System Design in Digital Circuits. Design Methodologies. | 3 |
| EE6307 | Analog Integrated Circuit Design | Review of Fundamentals. Analog Building Blocks. Switched Capacitor Circuits. Current Mode Circuits. Continuous-Time Filters. Data Converters. | 3 |
| EE6309 | VLSI Systems | 1. Data security, system noise considerations, and high-speed synchronization. 2. Memory organization and performance analysis, and concepts and techniques for parallel processing and pipeline processing. 3. VLSI system design verification and testability. | 3 |
| EE6601 | Advanced Wafer Processing | Dielectrics for CMOS technology. Chemical and mechanical polishing. Lithography and resist technology. Etching process and technology. Backend interconnect technology. Cleaning technology. Process integration. Metrology and analytical techniques. | 3 |
| EE6604 | Advanced Topics In Semiconductor Devices | Bipolar transistor operating principles. Bipolar device modeling. State-of-the-art bipolar structures. MOS device operation. MOSFET modeling. MOS device scaling effects. Semiconductor memories. Semiconductor heterojunctions and devices. New devices and future trends. | 3 |
| EE6610 | Integrated Circuit (IC) Packaging | Overview of IC & microsystems package. Design of IC Package. Thermal Management of IC Package. Fabrication of Single Chip and Multichip Package. IC Assembly, Sealing and Encapsulation. Failure Analysis and Reliability. Microsystems Package and Fabrication. System Level Package and Fabrication. | 3 |
General Elective Courses
| Course Code | Course Title | Course Content | AUs |
| EE6008 | Collaborative Research & Development Project | Project Charter, Design and prototype development, Project implementation, Testing and instrumentation, Project report, Oral presentation, Demonstration | 3 |
| EE6010 | Project Management & Technopreneurship | Project Initiation and Planning. Project Scheduling and Implementation. Project Monitoring, Control and Evaluation. Innovation and Entrepreneurship. | 3 |
| EE6102 | Cyber Security & Blockchain Technology | Cyber Security Threat Landscape, Industry 4.0 and Cyber Security, Cyber Security Education, Awareness and Compliance, Cyber Security Planning, Policies and Compliance, Cyber Security Risk Assessments and Biometric-based Security approaches, Public key Infrastructure (PKI), Web Security and role of firewalls and Intrusion Detection, Online Payment, and Cryptocurrencies. Basics of Blockchain technology, Types of blockchain Technology, Blockchain Technology Applications for Industry 4.0, use cases and real-world case studies | 3 |
| EE6128 | Rf Circuits For Wireless Communications | Microstrip Line and Network Parameters. Microwave Power Dividers and couplers. Microwave Filters. Amplifiers. Oscillators and Synthesizers. Detectors and Mixers. Frequency Multipliers and Control Circuits. RF Receiver Design. | 3 |
| EE6285 | Computational Intelligence | Introduction to Fuzzy Logic, Introduction to Fuzzy Sets, Introduction to Fuzzy Inference Systems, Fuzzy Logic Applications, Introduction to Genetic Algorithm, Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures, Neural Network Applications | 3 |
| EE6301 | Smart Biosensors & Systems For Healthcare | Introduction to biosensors and healthcare; Optical biosensors-fundamentals; Optical biosensors-applications; Biomedical imaging with optical technologies; Introduction to electrical biosensors- fundamentals; Introduction to electrical biosensors- fabrications; Applications of electrical biosensor; Introduction to bio-intelligent systems; Artificial intelligence in medical sensing and imaging | 3 |
| EE6303 | Electromagnetic Compatibility Design | EMC Regulatory Requirements. Non-Ideal Behaviors of Passive Components. Conducted EMI and Filter Design. Electromagnetic Shielding. Basic Grounding Concept. Crosstalk. Printed Circuit Board Layout and Radiated EMI. Electrostatic Discharge. Radio Frequency Interference. Emission and Susceptibility Measurement Methods. | 3 |
| EE6341 | Advanced Analog Circuits | The course introduces the students design methodologies of analog circuits and subsystems that are constructed using bipolar and MOS transistors. It covers five commonly used analog circuit modules: Wide-Bandwidth Amplifiers, Low Noise Amplifiers, Power Amplifiers, Active Filters and DC-DC Power Converters. | 3 |
| EE6406 | Analytic and Ensemble Learning | This course aims to equip students with a foundational understanding of machine learning from both analytic and ensemble perspectives. The curriculum is designed to offer an overview of the key technologies underpinning modern machine learning and deep learning. Initially, the course will recap the basic components of machine learning. It will then delve into fundamental learning techniques, including linear methods for regression, linear methods for classification, and polynomial plus kernel methods—all grounded in linear algebra. Subsequently, students will explore applied ensemble methods based on bagging and boosting. The course will cover classical ensemble methods such as Random Forest, AdaBoost, and standard Gradient Boosting, as well as more advanced boosting techniques, including XGBoost and LightGBM, which find practical applications in the industry. In addition, the course will also introduce reinforcement learning and its connection with ensemble learning. To highlight the practical relevance, the course will include examples from industrial contexts. | 3 |
| EE6407 | Genetic Algorithms & Machine Learning | Review of Combinatorics and Probability. Introduction of Genetic Algorithms. Differential Evolution. Particle Swarm Optimization. Advanced Techniques. Principles of Machine Learning. Paradigms of Machine Learning. Kernel Methods. | 3 |
| EE6483 | Artificial Intelligence and Data Mining | Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis | 3 |
| EE6497 | Pattern Recognition & Deep Learning | Introduction, probability review, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models, Self-supervised Learning. | 3 |
| EE6506 | Power Semiconductor Based Converter In Renewable Energy Systems | Module 1: Overview of power electronic circuits and semiconductor devices, Module 2: Power diodes and thyristors as switching devices, Module 3: Power transistors as switching devices 2, Module 4: Protection of devices from overheating di/dt, dv/dt, Module 5: Passive components and magnetics, Module 6: Renewable energy systems | 3 |
| EE6618 | Quantum Information & Engineering | Fundamental quantum information. Quantum qubit and algorithm. Silicon photonics and fabrication. Passive and active photonic device. Quantum key distribution and communication. Quantum computing and application. | 3 |
| EE6808 | LED Lighting & Display Technologies | Review of optoelectronic processes and optics. Review of solid state lighting and display technologies. Light-emitting diodes. Plasma display panels. Field emission displays. Liquid crystal displays. Organic light-emitting device. Thin film transistors and active-matrix backplane circuits. AC thin film electroluminescent displays and printed electrochromic displays. | 3 |
| EE7205 | Research Methods | Research Preparation and Planning. Research Sources and Review. Quantitative Methods for Data Analysis. Experimental research methods. Academic Writing & Presentation | 3 |
| EE7602 | Integrated Circuit Technology | Overview of Electronic Devices. Electronic Device Fabrication. Small Geometry Effects, Device Scaling and Advanced Nanoscale CMOS Devices. Latchup and ESD Protection in CMOS Technology. Failure Mechanisms of Integrated Circuits | 3 |
| EE7603 | Semiconductor Physics & Applications | Review of Fundamentals; Energy Bands of Semiconductors; Doping and Carrier Concentrations; Physics of Low Dimensional Systems; Electrical Transport Phenomena and Working principles and designs of nanoelectronic devices; Excess Carriers; Optical Properties and Photonic Devices. | 3 |
| EE7604 | Laser Technology | Laser Fundamentals. Laser Resonators. Laser Oscillation. Laser System Design. Laser Techniques. Semiconductor Lasers. Laser Applications. | 3 |
| EE7608 | Optoelectronics Engineering: Designing The Future with Light | Fundamental concepts, Electron spectrometrics, Phonon and photon spectrometrics, Applications, 2D materials and interfaces, Metamaterials and plasmonics | 3 |
| EE6311 | Integrated Nanophotonic Devices | This course aims to equip students with broad foundational knowledge of current research and technology trends in the photonics. The student will learn basic physical concepts and the working principles of various cornerstone nanophotonic devices, in addition to methods to describe and analyse their operation. The course is intended for those wishing to pursue careers in research, development, or engineering roles in nanophotonics fields such as integrated optics, optoelectronics, and photonics-based industries. | 3 |
| EE6312 | Integrated Photonic Chips for Environmental sensing and Biotechnology | Photonic sensors are electronic detectors that function by the conversion of light into electrical or biological signals. Such sensors have been fully integrated and widely used across industries for both simple and complex purposes including security, environmental sensing, and biotechnology. The heart of the chip hence plays a critical role in modern sensor technologies. This course aims to provide postgraduate students the key knowledge of integrated chips based on photonics and optoelectronics, from fundamental physics, material design, to device integration. This course will emphasize on the structures of nanoscale to quantum scale sensors in order to provide students with a fundamental background of optical/electrical interactions in biological systems and medical technology. Different active and passive nanophotonic and nanoelectronic devices and fabrication technologies will be covered followed by their applications in environmental monitoring, global sensing, and human machine biochips for health and bioindustry. Students will learn how to design and integrate a nanochip photonic sensing system to tackle human health and global challenges from an engineering perspective. | 3 |
| EE6313 | Mass Production & Integration of Nanomaterials | The course aims to address the key challenges of transitioning nanomaterial innovations from the laboratory to large-scale industrial production. It provides in-depth knowledge of advanced manufacturing processes, material characterization techniques, integration methodologies, and the economic factors driving the widespread adoption of nanomaterials. Through hands-on laboratory sessions, students will develop practical skills in nanomaterial synthesis and advanced characterization methods. Additionally, facility tours and talks from industry leaders will offer valuable perspectives on bridging the gap between research, development, mass manufacturing, and commercialization. | 3 |
| EE6314 | Nanotechnology for Energy Storage & Harvesting | This course will cover application of nanomaterials and nanotechnology for energy storage and harvesting applications. For the energy storage, students will learn the fundamentals of batteries including electrochemistry and recent advances of batteries using nanomaterials. For the energy harvesting, various type of energy harvesting methods will be covered. You will learn the fundamentals and practical application of kinetic and thermal energy harvesting systems using nanomaterials. Laboratory session will provide your direct experience of fabrication and characterization of battery to help immediate adaptation to industry and higher degree study in energy storage field. | 3 |
| EE6315 | Principles and Devices of Nanoelectronics | This course offers the foundations and latest advances in nanoelectronics for graduate students. It covers a broad range of fundamentals from low dimensional electronic materials to nano/microelectronic electronic device applications, including low-dimensional carbon, silicon and metal oxide nanoelectronic devices, organic semiconductor devices and spintronic devices. This course fills the gap between electronic industry and research and captures recent developments in micro/nanoelectronics. Students are expected to have not only an overall understanding of current micro/nanoelectronics, but also a wide range of fundamental and device level knowledge to design and study the devices. | 3 |
| EE6316 | Integrated Circuits for AI | This course provides an introduction to Integrated Circuit Design for Artificial Intelligence Applications, equipping students with the essential knowledge and skills to design AI circuits. It covers a wide range of foundational concepts, including basic AI models, artificial neural networks, computing-in-memory circuit design, and subthreshold IC design for neuromorphic hardware. By bridging the gap between AI theory and practical circuit implementation, this course lays a foundation for designing cutting-edge AI circuits, preparing students to innovate in this rapidly evolving field. | 3 |
| EE7207 | Neural Networks & Deep Learning | This course is intended to provide PhD students with an in depth understanding of the fundamental theories and learning methods, as well as advanced issues of neural networks and fuzzy logic systems. After the course, the students will be able to apply the learned knowledge to solve problems in their respective research fields. | 3 |
Special Term - General Elective Courses
| EE6131 | SPECIAL TOPICS IN COMMUNICATION NETWORKS: DESIGN & ANALYSIS OF ALGORITHMS | The course provides a foundation in algorithm design and analysis, focusing on problem-solving and computational efficiency. It prepares students to optimize solutions and implement effective algorithms, with rich discussions, examples and hands-on experiments on design and application of optimization algorithms in communication networks, smart grid, and IoT etc. |
| EE6230 | MULTI-ROBOT SYSTEMS | This course aims to equip you with a comprehensive understanding of the models, principles, methodologies, and technologies for multi-robot systems, focusing on their models, dynamics, control, information exchange, coordination, and optimization. This course is designed for graduate students with backgrounds in robotics, automation, control, computer science, electrical engineering, mechanical engineering, or related fields. By exploring real-world applications such as autonomous vehicles, swarm robotics, and collaborative industrial robots, students will gain both theoretical insights and practical skills essential for careers in robotics R&D, AI-driven automation, and emerging fields like human-robot interaction and autonomous systems. Whether preparing for cutting-edge research or tackling complex challenges in robotics-related industries, this course offers you a vital foundation for future innovation and impact. |
| EE6231 | SPECIAL TOPICS ON REINFORCEMENT LEARNING | Reinforcement learning is inherently interdisciplinary, combining principles from machine learning and control theory. The course aims to equip you with expertise in a cutting-edge AI field, empowering students with the skills needed for innovation, research, and impactful applications in academia and industry. After completing this course, you should understand core concepts and theories in reinforcement learning and implement and evaluate key reinforcement learning algorithms. You can apply the algorithms to real-world problems, especially in autonomous systems, robotics, financial technology, and healthcare. |
| EE6317 | CHIP SECURITY WITH MACHINE LEARNING | The objective of this course is to provide graduate students with an understanding of security challenges in chip design and manufacturing, and how machine learning can be used to address them. Students will learn to identify and mitigate hardware vulnerabilities such as hardware trojans, counterfeit ICs, and side-channel attacks. The course covers the chip development cycle, basic and advanced machine learning models, and their application in chip security analysis. Additionally, students will explore design-for-trust practices to ensure security from design to production. By the end, they will be able to analyze threats and apply secure design principles using machine learning. |
| EE6318 | POWER MANAGEMENT INTEGRATED CIRCUIT DESIGN | The Power Management Integrated Circuit (IC) Design course aims to equip students with a comprehensive understanding of the principles, analysis, and design of power management integrated circuits and systems. The course is an IC Design course focusing on DC-DC converter architectures, steady-state and AC modeling, control methodologies, and advanced power management IC designs. Students will develop the skills to analyze, model, and optimize power management circuits with an emphasis on efficiency, stability, and reliability. By integrating theoretical knowledge with practical design techniques, the course prepares students to tackle real-world challenges in modern power management IC applications. |
| EE6514 | SPECIAL TOPICS IN CLEAN ENERGY SYSTEM DESIGN | This course aims to equip you with hands-on, practical skills to design, simulate, and analyse clean energy systems such as solar photovoltaic, wind power, and energy storage systems using professional circuit simulation software. By bridging the gap between theoretical knowledge and real-world applications, the course provides essential skills and expertise demanded by the industry. If you are pursuing a career in renewable energy, power systems, or power electronics, this course will offer valuable experience in applying theoretical concepts to practical challenges, preparing you for advanced roles in clean energy system design and enabling you to contribute to sustainable energy solutions. |
| EE6515 | ENERGY STORAGE SYSTEMS & APPLICATIONS IN POWER SYSTEMS | This course aims to equip students with a comprehensive understanding of energy storage systems (ESS) and their critical role in modern power systems. By bridging technical, economic, and sustainability aspects, the course provides in-depth knowledge of ESS technologies, economic evaluation, market participation, and lifecycle management. It prepares students to tackle real-world challenges such as grid stability, renewable energy integration, and sustainable battery use. With a multidisciplinary approach and opportunities for industry collaboration, the course empowers students with advanced skills to innovate, lead, and contribute effectively to the evolving energy landscape and the global energy transition. |
Note: the above curriculum is subject to change.