For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ASE3005 | Advanced Semiconductor Design Technologies | 3 | 6 | Major | Bachelor | 3-4 | Advanced Semiconductor Engineering | Korean | Yes |
| This course explains the fundamental theories of each stage in semiconductor design and introduces the challenges faced at each stage as well as the methodologies developed or under development to address these challenges. The course focuses on major issues in semiconductor design, including Design-Technology Co-Optimization (DTCO), low-power design methodologies, design methodologies considering process variations (DFM & variability-aware design), 3D heterogeneous integration methodologies, and AI chip design methodologies. Additionally, it explores recent advancements in applying AI/ML (machine learning algorithms for artificial intelligence) techniques to semiconductor design automation. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| EAM4014 | Global Techno Management | 2 | 4 | Major | Bachelor/Master | 1-4 | Advanced Materials Science and Engineering | - | No |
| The requirement and problem of the real technology in the industrial field are analyzed by inviting specialists and CEOs to learn the ability of solving the industrial problem. Also, management and commencement of an enterprise are studied. | |||||||||
| ECE4223 | Semiconductor Process Technology | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This course helps to understand the overall semiconductor processes by introducing the theory and the application of unit processes; photolithography, photo-mask, dry-etch, cleaning, chemical-mechanical polishing(CMP), diffusion and thin film, and module processes; transistor, isolation, capacitor, interconnection. This also suggests the direction of process technologies for the future generations. | |||||||||
| ECE4237 | Robotics | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course discusses the kinematics and the dynamics of manipulators. The path planning of each joint and some control algorithms of manipulators are also discussed. | |||||||||
| ECE4238 | Linear Systems | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| Methods of analysis for continuous and discrete-time linear systems. Convolution, classical solution of dynamic equations, transforms and matrices are reviewed. Emphasis is on the concept of state space. Linear spaces, concept of state, modes, controllability, observability, state transition matrix, state variable feedback, compensation, decoupling are treated. | |||||||||
| ECE4246 | Digital Control | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | - | No |
| Many industrial control systems include digital computers as an integral part of their operation. Recent trends toward digital control of dynamic systems, rather than analog control, is mainly due to the recent revolutionary advances in digital computers and to advantages found in working with digital signals rather than continuous-time signals. Also, the availability of low-cost microprocessors and microcomputers established a new trend for even small-scale control systems to include digital computers to obtain optimal performance. The main purpose of this course is to present a comprehensive treatment of the analysis and design of discrete-time control systems. In particular, this course provides clear and easy-to-understand explanations for concepts involved in the study of discre-time control systems. | |||||||||
| ECE4249 | Computer Vision | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course focuses in the study of theories for image analysis. The first part consists of Image formulation model, early processing, boundary detection, region growing and segmentation, motion detection, merging and introduction of morphology. The second part, we cover basic concepts of statistical model, dis- criminant function, decision boundary and rules and neural network for visual pattern recognition. | |||||||||
| ECE4261 | Nano Device | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| In this course, we will cover many topics including the growth methods and the physical/electrical characteristic for sub-nanometer dimensional materials. With the basic knowledge on nano scale materials, we deal with the nano devices (e.g., memory, logic, display devices, etc.), nano structures and operation principles. | |||||||||
| ECE4269 | Advanced Display Engineering | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | - | No |
| This lecture deals with a various applications of display technology based on device physic and process. Thin film transistor device and process will be covered in detail to provide advanced knowledge in display technology. | |||||||||
| ECE4272 | Advanced Convergence Capstone Design | 3 | 6 | Major | Bachelor/Master | Electrical and Computer Engineering | Korean | Yes | |
| Design addresses problems faced by local communities with a focus on design, experimentation, and prototyping processes. Particular attention is placed on constraints faced when designing for handicapped and elderly people. Multidisciplinary teams work on projects in collaboration with community partners, field practitioners, and experts in relevant fields. Topics covered include design for affordability, design for sustainability, and strategies for working effectively with community partners and customers. Students work in multidisciplinary teams on term-long projects in collaboration with community partners, field practitioners, and experts in relevant fields. Students may continue projects begun in other problem-based learning projects (such as in convergence capstone design, global convergence capstone design). | |||||||||
| ECE4273 | Introduction to Mobile Communications | 3 | 6 | Major | Bachelor/Master | Electrical and Computer Engineering | English | Yes | |
| This course covers various topics on mobile communications, which include cellular systems, cognitive radio networks, heterogenous wireless networks, multiple access techniques (CDMA/OFDMA/FDMA/TDMA), wireless protocol, transmission techniques in wireless channels, synchronization techniques, design and management of mobile communication networks, frequency management and next generation mobile communication systems. | |||||||||
| ECE4274 | Technological Innovation and Business Management | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course covers four essential stages of the necessary and sufficient conditions for technological innovation and commercialization that make today's dreams become tomorrow’s daily life for everyone. The first stage is the preparation stage for gaining insight by reading and systematically analyzing the changes in the future, the second stage is systematically understanding innovative values and strategically devising SMART, and the third stage is successfully creating, communicating, and managing teams to innovate. The final stage covers the business essentials and business know-how and conditions that management should know. | |||||||||
| ECE4275 | Solid State Physics for Electrical Engineers | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| The fundamentals of solid-state physics is to be lectured, in order to design and develop the solid-state electron devices. To figure out the basic properties of insulator/semiconductor/conductor as well as how to draw the energy band diagram, the fundamentals on solid-state physics are provided. Afterwards, students can learn how carriers are moving in semiconductor (e.g., drift, diffusion, recombination-generation). Before taking the advanced classes such as semiconductor device engineering and nano device engineering, the theory for metal-semiconductor junction and p-n junction are to be studied in great detail (including DC, AC, and transient responses). | |||||||||