For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ECE4278 | SOC Design and Practice | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| This course delivers fundamentals of SoC design, including basic concepts, components, and design flows. It provides an introduction to SoC and its components. It also teaches design flows of SoC, including Register-Transfer-Level (RTL) design, verification, logic synthesis, formal verification, clocking, synchronous/asynchrouns signal interface. | |||||||||
| ECE4278 | SOC Design and Practice | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course delivers fundamentals of SoC design, including basic concepts, components, and design flows. It provides an introduction to SoC and its components. It also teaches design flows of SoC, including Register-Transfer-Level (RTL) design, verification, logic synthesis, formal verification, clocking, synchronous/asynchrouns signal interface. | |||||||||
| ECE4279 | Memory Semiconductor Design | 3 | 6 | Major | Bachelor/Master | 1-4 | English | Yes | |
| CMOS memory devices are known as traditional digital devices. In order to suffice tough requirements for advanced IC’s and artificial intelligence applications, new memory devices beyond conventional DRAM/SRAM/flash are suggested. This course starts from brief review of memory system, and expands to operating principles of memory devices, design practice, and performance metrics. New memories such as emerging non-volatile memories, content addressable memory, and process-in-memory are covered at the end of course. Practice on memory circuit design and evaluation is included as a part of homework assignment. | |||||||||
| ECE4279 | Memory Semiconductor Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| CMOS memory devices are known as traditional digital devices. In order to suffice tough requirements for advanced IC’s and artificial intelligence applications, new memory devices beyond conventional DRAM/SRAM/flash are suggested. This course starts from brief review of memory system, and expands to operating principles of memory devices, design practice, and performance metrics. New memories such as emerging non-volatile memories, content addressable memory, and process-in-memory are covered at the end of course. Practice on memory circuit design and evaluation is included as a part of homework assignment. | |||||||||
| ECE4280 | Wireless Networks Cornerstone | 3 | 6 | Major | Bachelor/Master | 1-4 | English | Yes | |
| This course provides an overview on wireless networking principles and technologies from the view point of computer science majors. The major themes will focus on the fundamentals and principles covering the protocol stacks of wireless networks, and wireless data services. | |||||||||
| ECE4280 | Wireless Networks Cornerstone | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This course provides an overview on wireless networking principles and technologies from the view point of computer science majors. The major themes will focus on the fundamentals and principles covering the protocol stacks of wireless networks, and wireless data services. | |||||||||
| ECE4281 | Flexible Electronic Materials | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| The course covers the basic concepts for understanding the electrical and mechanical characteristics of flexible functional electronic materials. Based the basic knowledge, we will learn fundamental technologies such as transfer-printing and printing processes for fabricating the flexible electronic devices. Based on the basic knowledge originating from Electric Circuits, Logic Circuits, and Solid State Electronic Devices, we will understand how RLC filters/amplifiers, transistors, logic gates, resistive random access memory devices stably operate under tensile/compressive strains. The aim of this class is to provide opportunities for learning the application to flexible display modules, sensors, and wireless integrated circuits. | |||||||||
| ECE4281 | Flexible Electronic Materials | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| The course covers the basic concepts for understanding the electrical and mechanical characteristics of flexible functional electronic materials. Based the basic knowledge, we will learn fundamental technologies such as transfer-printing and printing processes for fabricating the flexible electronic devices. Based on the basic knowledge originating from Electric Circuits, Logic Circuits, and Solid State Electronic Devices, we will understand how RLC filters/amplifiers, transistors, logic gates, resistive random access memory devices stably operate under tensile/compressive strains. The aim of this class is to provide opportunities for learning the application to flexible display modules, sensors, and wireless integrated circuits. | |||||||||
| ECE4282 | AI Integrated Circuits Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| In this course, AI Integrated Circuits are covered. Especially, recent research trends of the Processor In Memory (PIM) with the good energy efficiency will be dealt and design practice using the EDA tools will be done. | |||||||||
| ECE4283 | Intelligent System Integrated Circuit Design | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| In this course, intelligent system integrated circuits are covered. Especially, recent research trends of the intelligent power management circuit, AI based sensor signal processing circuits will be dealt and design practice using the EDA tools will be done. | |||||||||
| ECE4284 | Automotive Embedded Software | 3 | 6 | Major | Bachelor/Master | English | Yes | ||
| This course describes automotive embedded software and automotive software standard architecture, AUTOSAR Classic. The aim is to understand the main characteristics of automotive embedded software and to get hands-on experience developing software based on AUTOSAR Classic. | |||||||||
| ECE4285 | Theory and coding of generative AI | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| Along with the rapid advancement of AI through deep learning in recent years, generative artificial intelligence (AI) models like Stable Diffusion and ChatGPT are rapidly infiltrating various industries, thus being expected to lead the Fourth Industrial Revolution. In this educational program, the goal is to understand the theory and principles behind these deep learning and generative AI models. Students will develop practical skills necessary for utilizing these powerful technologies through computer programming practical applications and projects. Specifically, this course covers: (1) Understanding and hand-on coding of deep learning models such as CNN, YOLO, Semantic Segmentation, and GANs. (2) Learning the mathematical explanation and practical applications of Diffusion models, including understanding the principles of image generation through the analysis of open-source code of Stable Diffusion. (3) Understanding language generation models like Seq2Seq and Transformer architecture used in ChatGPT, and practical applications in natural language processing and related fields. Through this course, students will gain the knowledge of current and future AI technologies and acquire the skills needed to address real-world problems. | |||||||||
| ECE4286 | Artificial intelligence semiconductor memory device | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Artificial intelligence algorithms based on deep neural networks (DNN: Deep Neural Networks) are demonstrating performance that is similar to or exceeds that of humans in many complex cognitive tasks. However, the energy efficiency of current Neumann computing systems implementing deep neural network algorithms is very low compared to the human brain. To solve this problem, neuromorphic hardware, which consists of densely connected parallel neurons and synapses that mimics the brain neural network structure, was proposed. The objective of this lecture is to deliver a driving principles of neuromorphic hardware and memristor memories, which expresses the weight of synapses in neuromorphic hardware. It will be introduced the operating principles of various memristors, including floating gate memory type, resistive memory type, phase change memory type, magnetic random access memory type, and ferroelectric memory type. | |||||||||
| ECE4286 | Artificial intelligence semiconductor memory device | 3 | 6 | Major | Bachelor/Master | Electrical and Computer Engineering | - | No | |
| Artificial intelligence algorithms based on deep neural networks (DNN: Deep Neural Networks) are demonstrating performance that is similar to or exceeds that of humans in many complex cognitive tasks. However, the energy efficiency of current Neumann computing systems implementing deep neural network algorithms is very low compared to the human brain. To solve this problem, neuromorphic hardware, which consists of densely connected parallel neurons and synapses that mimics the brain neural network structure, was proposed. The objective of this lecture is to deliver a driving principles of neuromorphic hardware and memristor memories, which expresses the weight of synapses in neuromorphic hardware. It will be introduced the operating principles of various memristors, including floating gate memory type, resistive memory type, phase change memory type, magnetic random access memory type, and ferroelectric memory type. | |||||||||
| ECE5237 | Master＇s Research Problem I | 3 | 6 | Major | Master | 1-4 | Korean,English | Yes | |
| Performs research on a topic assigned by his or her advisor for his Master's degree. | |||||||||