Highlights
Junhee Sim, a Ph.D. candidate in the School of Electrical Engineering (ADNC Lab, advised by Professor Kyung Cheol Choi), received the Gold Award of the KIDS Award at the 2026 International Meeting on Information Display (IMID 2026).
IMID, hosted by the Korean Information Display Society (KIDS) and the Society for Information Display (SID), is a globally recognized international conference in the field of display technologies. This year, IMID 2026 was held at BEXCO in Busan from August 18 to 21, 2026. The KIDS Award, sponsored by LG Display and Samsung Display, recognizes outstanding papers based on the innovation and excellence of their research in the display field. The Gold Award is the highest honor of the KIDS Award and is presented to only two papers.
Junhee’s award-winning paper, titled “Artificial Neural Network-based Optical Inverse Design of White Tandem Organic Light-Emitting Diodes”, proposes an artificial intelligence-based inverse design approach for optimizing the optical structure of white tandem organic light-emitting diodes (White Tandem OLEDs), which consist of complex multilayer thin-film structures.
This award recognizes the excellence of AI-based optical design technology for displays and its potential for application in the design of next-generation display devices.
A researcher in Chicago remotely controls a miniaturized brain implant in Daejeon, Korea — over the internet. Korean researchers have developed a wireless device that can deliver drugs and light to precisely modulate targeted neurons from anywhere in the world. The technology is expected to overcome the constraints of distance and location, supporting long-term studies of brain disorders and the future development of therapeutic devices.
A research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering, in collaboration with Professor Wha Young Kim’s team at Yonsei University College of Medicine, has developed an IoT-enabled wireless neural implant that integrates drug delivery, optical stimulation, wireless communication, and internet-based remote control into a single miniaturized device.
Conventional studies involving optical stimulation or drug delivery to the brain often required bulky equipment connected by wires, restricting the natural movement of experimental animals. Even wireless devices had their own limitations, often requiring researchers to operate them at close range, thereby restricting experimental flexibility and introducing the so-called “observer effect”.
To overcome these limitations, the research team developed the brain implant with IoT connectivity. Even without being physically present in the laboratory, researchers can remotely administer drugs or stimulate specific brain neurons with light in real time via the internet. The device can also be programmed to operate automatically at a preset time.
The device is about the size of a sugar cube and is designed not to interfere with the animal’s natural behavior. Researchers no longer need to repeatedly approach or handle equipment near the animal, reducing the stress caused by a researcher’s presence, which can otherwise affect the animal’s behavior and bias experimental results.
The implant contains a microfluidic system that precisely delivers drugs to a targeted region of the brain, as well as a micro-LED that enables optical control of specific neurons. Drug delivery and optical stimulation can be controlled independently, or the two functions can be combined.
The drug reservoir is designed to be magnetically detachable. Even after the drug is depleted, researchers can replace or refill the reservoir without the need for additional implantation surgery, enabling long-term, repeated experiments.
The research team implanted the device in rats and verified its performance over a four-week period. In particular, a researcher in Chicago successfully operated the brain implant in Daejeon, Korea, in real time via the internet, demonstrating that the device can operate reliably over intercontinental distances.
The team also conducted an experiment in which cocaine was wirelessly administered to a rat’s brain while specific neurons were simultaneously stimulated with light. The results showed that addiction-related behavioral responses could be suppressed, demonstrating the potential of combining drug delivery and optical stimulation for neural circuit research.
By eliminating the need for researchers to operate equipment directly beside experimental animals, this technology enables long-term studies of the relationship between brain circuits and behavior under naturalistic conditions. It is expected to be useful for studying conditions that involve long-term changes in neural circuit function and behavior, such as addiction, depression, and neurodegenerative diseases.
The technology could ultimately pave the way for intelligent implantable medical devices that combine brain-state sensing with AI to deliver drugs or neural stimulation precisely when needed.
Professor Jae-Woong Jeong from KAIST said, “This technology transforms wireless brain implants that use light and drugs from short-range control tools into IoT-based brain engineering platforms capable of long-term, automated, and remote experimentation.” He added, “In the long term, it could contribute to the development of intelligent implantable medical devices for the diagnosis and treatment of brain disorders.”
Professor Wha Young Kim from Yonsei University said, “This platform allows researchers to remotely and precisely control specific brain circuits over extended periods while animals move freely under naturalistic conditions.” She added, “It is expected to become an important tool for identifying causal relationships between neural circuits and behavior in disease models such as addiction, depression, and neurodegenerative disorders.”
Eun Young Jeong, a doctoral student in KAIST’s School of Electrical Engineering, and Jong Woo Park, a doctoral student at Yonsei University College of Medicine, served as co-first authors. The study was published on July 29 in the international journal Science Advances.
- Paper title: IoT-enabled wireless neural implant for chronic, programmable neuropharmacology and optogenetics,
- DOI: 10.1126/sciadv.aee8648
This research was supported by the Mid-Career Researcher Program and Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, as well as the Industrial Technology Alchemist Project of the Ministry of Trade, Industry and Energy.
As AI systems become more advanced, memory is required to transfer larger amounts of data at higher speeds. But conventional planar semiconductor scaling is running out of room. A KAIST research team has now addressed a key weakness in three-dimensional, vertically stacked memory devices, opening a new path to faster, more power-efficient AI semiconductors.
The research team led by Professor Jimin Kwon from the School of Electrical Engineering has developed a new multilayer interlayer dielectric structure that reduces defects and significantly enhances the performance of oxide vertical channel transistors (VCTs), a next-generation memory device. The study was conducted in collaboration with researchers from UNIST, Yonsei University, and other Korean institutions.
DRAM, which serves as the main memory in computers, has advanced over the past several decades by scaling down device size while reducing power leakage. More recently, vertical channel structures, in which current flows vertically, have become a key technology for increasing memory density.
The challenge is oxygen vacancies — defects caused by the absence of oxygen atoms in the oxide semiconductor — which destabilizes the material’s electrical properties. But oxygen cannot simply be supplied without limit: when oxygen is supplied to suppress oxygen vacancies, some of the oxygen tends to migrate further, reaching the metal electrode and oxidizing it, which degrades device performance instead. The channel needed oxygen; the electrode did not. Therefore, selectively controlling oxygen flow became a key challenge.
The KAIST team developed a new multilayer interlayer dielectric consisting of silicon nitride/silicon dioxide/silicon nitride (SiN/SiO₂/SiN), engineered to function as an “oxygen tunnel” that steers oxygen selectively toward the channel while blocking its path to the electrode. The structure enabled stable compensation of oxygen vacancies in the oxide semiconductor while simultaneously suppressing unwanted oxidation at the electrode, thereby resolving the trade-off.
As a result, the researchers achieved world-class current density and data retention time in oxide vertical channel transistors.
The device also demonstrated outstanding operational stability. Even after more than ten million cycles of harsh electrical stress testing, the threshold voltage shift remained below 50 millivolts (mV), confirming its high reliability as a memory device.
The team further evaluated system-level performance by integrating conventional silicon CMOS technology with the new oxide semiconductor platform. The results suggest that this approach could significantly improve the performance of next-generation compute-in-memory (CIM) systems, intelligent semiconductors that perform AI computation directly inside memory.
Hyeonho Gu, the first author of the study, said, “This research is significant because it goes beyond improving memory density and addresses the long-standing instability problem in 3D devices through a new approach based on oxygen migration control.” He added, “We expect this technology to play a key role in accelerating the commercialization of ultra-low-power, high-performance compute-in-memory systems required for the AI era.”
This study was led by KAIST researcher Hyeonho Gu as the first author and was published on May 20 in Advanced Functional Materials, a leading international journal in materials science. The paper was also selected as a Front Cover article in recognition of its academic significance and originality.
- Paper title: Oxygen-Tunnel Indium Tin Oxide Vertical Channel Transistors with Enhanced Current Density and Reliability for Monolithic 3D Compute-In-Memory Systems
- DOI: https://doi.org/10.1002/adfm.202531989
- Author information: Hyeonho Gu (KAIST, first author); Yongwoo Lee (KAIST, corresponding author); Haksoon Jung (KAIST, corresponding author); Jimin Kwon (KAIST, corresponding author); Hoichang Jeong (UNIST, co-author); Yanfeng Zhao (UNIST, co-author); Heesoo Yang (UNIST, co-author); Minho Park (UNIST, co-author); Hyeonjin Lee (UNIST, co-author); Seunghun Baek (UNIST, co-author); Minju Song (UNIST, co-author); Junghwan Kim (UNIST, co-author); Youngmin Jo (KAIST, co-author); Hyunjin Park (Korea Research Institute of Chemical Technology, co-author); Munhyeon Kim (Seoul National University of Science and Technology, co-author); Jae-Joon Kim (Seoul National University, co-author); Kyuho Jason Lee (Yonsei University, co-author); and Byungjo Kim (UNIST, co-author).
This research was supported by the National Semiconductor Laboratory Program and the Excellent Young Researcher Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT; the Broadcast and Telecommunications Industry Technology Development Program of the Institute of Information & Communications Technology Planning & Evaluation; and the Super Gap Technology Development Program of the Korea Evaluation Institute of Industrial Technology, funded by the Ministry of Trade, Industry and Energy.
We are pleased to announce that Dr. Haeyong Kang, a former member of Prof. Chang D. Yoo’s laboratory (U-AIM) in the School of Electrical Engineering at KAIST, has been appointed as a faculty member in the School of Digital Software Engineering at Duksung Women’s University, effective September 1, 2026.
After graduating from high school in Korea, Dr. Kang pursued his bachelor’s and master’s degrees as Korea-Japan Joint Government Scholarship Program’s student in Japan before completing his Ph.D. in Electrical Engineering at KAIST. Following his Ph.D., he embarked on an entrepreneurial journey, spending several years commercializing AI technologies and achieving a successful startup exit before returning to academia. His career thus brings together diverse academic experiences in Korea and Japan with hands-on experience in entrepreneurship and technology commercialization.
Dr. Kang’s research interests include Large Language Models (LLMs), Continual Learning, and Parameter-Efficient Fine-Tuning (PEFT). His research has been published at leading international AI conferences, including ICML and ICLR, as well as in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
At Duksung Women’s University, Dr. Kang plans to expand his research toward Continual Adaptive AI, AI Agents, and Physical AI, with the goal of developing intelligent AI systems capable of continuously learning and adapting to changing environments and new experiences.
We sincerely congratulate Dr. Kang on his faculty appointment and wish him great success in this exciting new chapter of his academic career.
Dr. Youngjoon Yu, an alumnus of the Integrated Vision Language (IVL) Lab (Advisor: Prof. Yong Man Ro) has been appointed as a tenure-track faculty member in the School of Computer Science at Dongduk Women’s University, effective September 1, 2026.
Dr. Yu received his B.S. degree in Electrical Engineering from KAIST, followed by an M.S. degree from the KAIST College of Business, and subsequently earned his Ph.D. degree in Electrical Engineering from KAIST. After completing his doctoral studies, he worked as a postdoctoral researcher at the Center for Applied Research in Artificial Intelligence (CARAI), KAIST, where he conducted research on reliable artificial intelligence for defense applications, with a particular focus on the integration and deployment of heterogeneous sensor information in real-world environments.
His doctoral dissertation, entitled “Study on vision-language bias mitigation with causal inference,” investigated methods for identifying and mitigating data biases and spurious correlations that arise when artificial intelligence models interpret visual information and perform language-based reasoning. In particular, his research has focused not only on improving predictive accuracy, but also on advancing Trustworthy AI by enabling AI systems to provide more reliable and interpretable reasoning while reducing hallucinations and erroneous inferences.
His research contributions have been published in leading international journals in computer vision and artificial intelligence, including IEEE Transactions on Image Processing and IEEE Transactions on Neural Networks and Learning Systems, demonstrating the academic significance and quality of his work.
At the KAIST Center for Applied Research in Artificial Intelligence, Dr. Yu further investigated trustworthy AI technologies designed for complex and resource-constrained defense environments involving diverse sensing modalities. More recently, he has expanded his research toward multimodal sensor fusion and artificial intelligence, with an emphasis on enabling AI systems to accurately understand and integrate information from heterogeneous sensors, including RGB cameras, thermal imaging, and depth sensors.
At Dongduk Women’s University, Dr. Yu plans to pursue research and education centered on Causal AI and Trustworthy AI. In particular, in the emerging era of Physical AI, his research will focus on developing intelligent systems and robots capable of integrating multimodal sensory information from RGB cameras, thermal imaging, depth sensors, hyperspectral sensors, and other sensing modalities while minimizing bias and hallucination. His ultimate goal is to develop AI technologies that can reliably perceive, reason about, and make decisions in complex physical environments.
We warmly congratulate Dr. Yu on this new chapter of his academic career and look forward to his continued contributions to education, research, and academia–industry collaboration at Dongduk Women’s University.
< Dr. Hyun-Ho Kim >
Dr. Hyun-Ho Kim, an alumnus of the Video and Image Computing (VIC) Lab in the KAIST School of Electrical Engineering (Advisor: Prof. Munchurl Kim), has been appointed as an Assistant Professor in the Department of Electronics Engineering at Chungnam National University, effective September 1, 2026.
Dr. Kim received his B.S. in Electronics and Radio Engineering from Kyung Hee University and his M.S. and Ph.D. degrees from the School of Electrical Engineering at KAIST under the supervision of Prof. Munchurl Kim.
Since December 2017, Dr. Kim has worked at the Korea Aerospace Research Institute (KARI), first as a researcher and later as a senior researcher, conducting research on image processing technologies for satellite image calibration and validation, quality enhancement, and applications. His research has focused particularly on deep learning-based PAN-sharpening, satellite image restoration and enhancement, compression artifact removal, and SAR-to-EO image translation.
He has published his research in leading international journals, including IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing, and IEEE Geoscience and Remote Sensing Letters. His work proposing a new learning approach for PAN-sharpening was also accepted at the European Conference on Computer Vision (ECCV 2026), demonstrating his active contributions to the fields of satellite imaging and computer vision.
At Chungnam National University, Dr. Kim will pursue research and education in deep learning, computer vision, image processing, and remote sensing. In particular, he plans to develop reliable AI-based image processing technologies that account for the characteristics of real-world satellite sensors and operational environments. Through this work, he aims to contribute to solving practical challenges in the space sector by improving the quality and utilization of satellite and remote sensing imagery.
We sincerely congratulate Dr. Kim on his faculty appointment and look forward to his future achievements.
Dr. Youngjoon Lee, an alumnus of the Advanced Radio Technology (ART) Lab in the KAIST School of Electrical Engineering (Advisor: Prof. Joonhyuk Kang), has been appointed as a tenure-track faculty member in the School of Software at Kwangwoon University, effective September 1, 2026.
Dr. Lee received his B.S. in Electrical and Electronic Engineering from UNIST and his M.S. in Electrical Engineering from KAIST. After completing his master’s degree, he worked as a researcher at government-funded research institutes. In particular, he addressed practical defense challenges as a researcher in the Military Capability Assessment Division of the Center for Military Analysis and Planning at the Korea Institute for Defense Analyses (KIDA). He then returned to KAIST to pursue his Ph.D., extending the research questions he encountered in practice through academic study. His doctoral dissertation, “Data-Free Early Stopping Framework for Practical Federated Learning,” investigates how to determine an appropriate stopping point for federated learning without relying on a separate validation dataset.
At KIDA, Dr. Lee contributed to research supporting military capability assessment and defense decision-making using analytical methodologies, including modeling and computer simulation. He also leveraged defense AI, data analytics, and wargaming simulations to support defense policy and force planning, while investigating reliable analytical methods suited to constrained defense environments.
At Kwangwoon University, Dr. Lee will pursue research and education in practical and privacy-preserving AI, with the broader goal of developing methods that can be applied effectively in real-world environments.
We sincerely congratulate Dr. Lee on his faculty appointment and look forward to his future achievements.
Dr. Gyoseung Lee, a graduate of the Intelligent Communication Systems Lab. (ICL) in the School of Electrical Engineering (Advisor: Prof. Junil Choi), has been appointed as an Assistant Professor in the Division of Semiconductor and Electronics Engineering at Hankuk University of Foreign Studies, effective September 1, 2026.
Dr. Gyoseung Lee received a Ph.D. degree from the School of Electrical Engineering at KAIST in February 2026. He then joined the C&M Standard Laboratory in the CTO division of LG Electronics Inc., where he worked on advanced 6G technologies related to integrated sensing and communication.
His main research focuses on the development of channel estimation and beamforming technologies for next-generation wireless communication systems. He has published numerous papers in top-tier journals such as IEEE Transactions on Wireless Communications and IEEE Transactions on Communications, and has received multiple best paper awards, demonstrating the excellence of his research.
Moving forward, he will continue his research on physical-layer technologies to improve the performance of next-generation wireless communication systems.
