For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ISS3290 | Introduction to Big Data Analysis | 3 | 6 | Major | Bachelor | English | Yes | ||
| Understand the genesis of Big Data Systems • Understand practical knowledge of Big Data Analysis using Hive, Pig, Sqoop • Provide the student with a detailed understanding of effective behavioral and technical techniques in Cloud Computing on Big Data • Demonstrate knowledge of Big Data in industry and its Architecture • Learn data analysis, modeling and visualization in Big Data systems | |||||||||
| SEE4001 | Special topics in semiconductor devices and processing | 3 | 6 | Major | Bachelor/Master | 1-4 | Semiconductor Convergence Engineering | Korean | Yes |
| On-site processing required for semiconductor device manufacturing experts give lectures directly so that students acquire the knowledge they need in the field. Additionally, through this lecture, students understand the physical operating principles and structure of manufacturing equipment. As semiconductor devices become highly integrated, we look at recent process and equipment development trends and problems with current processes that have become issues. | |||||||||
| SEE4002 | Artificial Intelligence Semiconductor Process Technology | 3 | 6 | Major | Bachelor/Master | 1-4 | Semiconductor Convergence Engineering | - | No |
| This course helps to understand the overall artificial intelligence 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 artificial intelligence semiconductor process technologies for the future generations. | |||||||||
| SEE4003 | AI Semiconductor Device Simulation | 3 | 6 | Major | Bachelor/Master | 1-4 | Semiconductor Convergence Engineering | Korean | Yes |
| We introduce the latest semiconductor technologies such as ultra-low power logic operation and multi-function memory operation, which are the characteristics required for semiconductors to run artificial intelligence algorithms, and optimize semiconductor technology through actual simulation based on our understanding of these. The electrical characteristics of 3D semiconductor devices due to semiconductor device scaling, ultra-low power semiconductor devices due to new charge transport mechanisms, and memory devices based on various new devices are confirmed through actual simulation exercises and optimized and adjusted by adjusting device design parameters. Check the impact on the operation of the artificial intelligence algorithm. Through this course, you can develop an understanding of the latest semiconductor device theory, especially artificial intelligence semiconductor device theory, and develop not only theory but also basic practical skills by selecting practical topics that can be frequently experienced in the actual industry and carrying out projects. There is a purpose. Prerequisite courses include physical electronics, semiconductor engineering, semiconductor device design, and electronic circuits. | |||||||||
| SFC4001 | Smart Factory Capstone Design 1 | 3 | 6 | Major | Bachelor/Master | Smart Factory Convergence | Korean | Yes | |
| This corporate-sponsored projects course in smart factory is an industry-university partnership that integrates design, manufacturing, service engineering, and business realities into the engineering curriculum. Students take their project ideas from concept to reality by designing, prototyping, and simulating real solutions in state-of-the-art facilities. This course challenges students to apply the knowledge and tools acquired during their undergraduate education to solve real-world engineering problems. | |||||||||
| SFC4002 | Smart Factory Capstone Design 2 | 3 | 6 | Major | Bachelor/Master | Smart Factory Convergence | Korean | Yes | |
| This course provides a unique opportunity for industry to partner with our university to educate the next generation of world-class engineers. Interdisciplinary teams of students work together to tackle projects sponsored by industrial clients. These teams collaborate with engineering faculty, who serve as mentors and advisers, to devise ideas to solve engineering problems. | |||||||||
| SSE3057 | Display Device and Process | 3 | 6 | Major | Bachelor | 4 | Semiconductor Systems Engineering | - | No |
| The subject aims to instruct the undergraduate students who major the display technology in regard to the display structure, display optics, and image quality etc. The details of its contents are listed as follow; 1. The electro-magnetic fields related to display-device functionality. - relation beween materials and electric field, anisotropic materials and dielectric response etc. 2. The display optics related to displaying mechanism.- Polarization optics in anisotropic-multi layered media, birefringence, electro-luminescence etc. 3. Colorimetry and color science including various color coordinates, image data and color, color gamut and accuracy etc. 4. Image quality analysis on display device: static image, moving image, off-axis image and 3D image quality. 5. Optics in liquid crystal (LC) materials, LC material properties 6. LCD modes and their mechanism, 7. OLED luminescence mechanism and image quality of OLED. 8. Main factors in display image quality and related device technologies to improve them. 9. 3D technology and its mechanism, remaining issues. 10. Niche display technologies including E-ink, cholesteric display, electro-wetting display etc. and their applicability and issues | |||||||||
| SWE3011 | Introduction to Artificial Intelligence | 3 | 6 | Major | Bachelor | 4 | Computer Science and Engineering | English,Korean | Yes |
| This course focuses on foundation of theory and introduction of advanced topics. Detailed subjects for theory are problem representation in state space, search strategy including breadth first search, depth first search and heuristic searchand knowledge representation methods such as using predicate logic, resolution and using rules. Advanced topics planning system (STRIPS), neural network and fuzzy techniques such as perceptron and hopfield network with learning methods, computer vision techniques such as image representation, edge detection, line and curve detection are also introduced. Finally, we introduce symbolic programming language, LISP with examples. | |||||||||