For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ECE5976 | Measurement Science | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| The course is designed to introduce students about measurement and instrumentation used in all fields of engineering. The course introduces the process of associating numbers with physical quantities and phenomenon. A measurement system can be interpreted as a channel for transporting information from a measurement object to a target object. In a measurement-and-control system (part of) the target object is identical to the measurement object. Three main functions can be distinguished—data acquisition, data processing, and data distribution. Data acquisition is used to obtain information about the measurement object and convert it into an electrical signal. Data processing includes processing, selecting, or otherwise manipulating measurement data in a prescribed manner. Data distribution is used to supply processed data to the target object. Multiple outputs indicate the possible presence of several target instruments such as a series of control valves in a process installation. Rapid growth in computers and computer science has contributed to an expansion of software tools for the processing of measurement signals and the computer is gradually taking over more and more instrumental functions. The case studies are drawn from the fields of electrical and mechanical engineering, control systems, and, satellite and car navigation technologies, and offer a broad variety of applications for all users. | |||||||||
| ECE5977 | Video Signal Processing | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Video signal processing is very importantly and widely used in multimedia system, smart phone/television/digital camcoder/camera/display, etc. In this class, students will study various theories and technologies for digital video signal representation and processing required in the whole process from video data acquisition to video display such as spatial-temporal video acquisition, representation format, motion estimation and segmentation, linear and non-linear filtering, video encoding and decoding, and video quality enhancement, etc. | |||||||||
| ECE5978 | Power Delivery Network Modeling and Design in Low Power Electronic Systems | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| In VLSI (SoC) designs, the power distribution network is an important design factor that must be considered to properly power up the transistors, and also affecting signal integrity, mixed system noise problems, and electromagnetic compatibility circuit operation. In addition, as the power supply voltage of low-power SoCs are lowered, the allowable error is reduced, requiring more understanding of the power distribution network. Since the supply power comes from off-chip, knowledge of the internal circuits and interconnects of the chip, package and PCB components, is required. This course introduces the theory and simulation tools that can be used for the design and analysis of VLSI power distribution network. | |||||||||
| ECE5979 | Introduction to Flexible Electronics | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| Flexible/stretchable electronics based on high-performance micro/nanomaterials have attracted a lot of attention not only for their fundamental scientific interest, but also for their many technological applications. This course reviews fundamental charge transport phenomenon in semiconductors and their MOSFET applications. Furthermore, this course deals with the current status of flexible/stretchable electronic materials/devices through the small size of group discussion. Each of specific topics is chosen from the various state-of-the-art research activities in flexible/stretchable materials/devices and self-healing bio-integrated electronics. 1. Electrical property of semiconducting materials 2. Electrical property of semiconducting devices 3. Electrical and mechanical properties of stretchable materials 4. Electrical and mechanical properties of stretchable electronic devices 5. Application of stretchable electronics to bio-integrated electronic systems | |||||||||
| ECE5980 | Advanced Topics in Semiconductor Devices and Circuits | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| In this course, advanced topics in the field of semiconductor devices and circuits are to be introduced and discussed. Various issues in novel devices and circuits/systems (e.g., Artificial intelligence semiconductor chips, bio-inspired/-integrated devices(DNA Memory) are the main topics in this course. | |||||||||
| ECE5981 | Neuromorphic Integrated Circuits and Systems | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| The purpose of this subject is to learn a variety of techniques for designing neuromorphic systems mimicking the structure of a biological brain and accelerating the computation of AI algorithms. After understanding the structure of a biological brain and the operation principle of AI algorithms, students will learn about the basic properties of memristive devices and CMOS circuits as important tools to implement neuromorphic systems. The lecture also deals with the characteristics of state-of-the-art neuromorphic integrated systems and learns how to design low-power high-performance neuromorphic systems. | |||||||||
| ECE5982 | IoT System IC Design | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| This course is the fusion course to cover the seven key technologies of IoT System Integrated Circuits (IC), Sensor Device and Signal Processing, Wireleline/Wireless Connectivity, AI based Data Processging, Energy Harvesting and Power Management, Memory Design, Security, System Application as team teaching form. | |||||||||
| ECE5983 | Circuits and Systems for 6G Communication | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course is the fusion course to cover key aspects of 6th generation mobile communication, including high-speed modulation and demodulation techniques, mobile and base-station transceiver architectures and design of various building-block circuits. | |||||||||
| ECE5984 | Foundations of Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| Machine Learning is the study of how to build computer systems that learn from experience. This course will give an overview of many models and algorithms used in modern machine learning, including generalized linear models, multi-layer neural networks, support vector machines, Bayesian belief networks, clustering, and reinforcement learning. | |||||||||
| ECE5984 | Foundations of Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| Machine Learning is the study of how to build computer systems that learn from experience. This course will give an overview of many models and algorithms used in modern machine learning, including generalized linear models, multi-layer neural networks, support vector machines, Bayesian belief networks, clustering, and reinforcement learning. | |||||||||
| ECE5985 | Quantum-Meta Optics | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course will improve a physical understanding of photons, which represent quantum concept of light. In particular, non-classical properties of photons such as spontaneous emission, quantum cryptography, and quantum teleportation are discussed. In addition, meta-surfaces and materials in integrated quantum optics are studied. | |||||||||
| ECE5986 | Digital Communication ICs and Systems | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course teaches fundamental electrical issues in the design of high-performance digital communication systems. The detailed topics include transmission line analysis; noise in digital systems, its effect on signaling, and methods for noise reduction; timing conventions; timing noise, its effect on systems, and methods for mitigating timing noise; synchronization issues and sychronizer design; clock and power distribution problems and techniques; building blocks of high-speed signaling systems (PLL, CDR, and I/O circuits). Prerequisites: electronic circuits, electromagnetics | |||||||||
| ECE5987 | Advanced Memory Systems | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| The memory system is critical to the performance of modern computers. As processors become more capable of handling large amounts of data, designing efficient memory systems becomes increasingly important. This course introduces the fundamental concepts of a memory hierarchy, which includes on-chip cache, main memory, and storage. We also go over the most recent research on each component of the memory systems. The goal of the course is to teach students how to design novel memory system architectures and evaluate design trade-offs using system-level simulation. | |||||||||
| ECE5988 | GPU Architecture Cornerstone | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Graphics Processing Units (GPUs) are the parallel processor that efficiently process large amounts of data. The GPUs are one of the key hardware that contribute the fast imrprovement of AI techniques n this class, we study the basic architecture of GPUs. Also, we have in-class presentations and discussions of the papers about the GPU architectures that were published in top-tier computer architecture conferences. By doing them, we study the recent research trends of GPU architectures. Also, we study the behavior of the GPUs with a GPU simulator. | |||||||||
| ECE5989 | Network System SW Design | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| As the performance of network hardware has improved significantly, it is important to understand the performance of system software for network processing. In this course, we learn the core designs of the current network software stacks including Linux kernel stacks and the most recent user-space stacks for high performance. We also have a term-project that designs and implements our own network protocols and algorithms (e.g., TCP congestion control) in Linux kernel. The recommended prerequisites for this course include computer networks and C programming language. | |||||||||