Kaist

The long standing commercialization challenge of l..

<(From Upper Left) Professor Nam Soon Choi, Professor Seungbum Hong, Professor Sang Kyu Kwak, (From Below Left) y Jeong-A. Lee, Haneul Kang, Yoonhan Cho, Seong Hyeon Kweon, Seonghyun Kim> As the electric vehicle era enters full scale, demand is increasing for batteries that can travel farther and last longer. Lithium-metal batteries have been attracting attention as a next-generation technology capable of surpassing the capacity limits of existing lithium-ion batteries. However, during the charging process, needle-shaped crystals called “dendrites” grow, shortening battery life and increasing the risk of fire, which has been identified as the biggest obstacle to commercialization. A Korean research team has developed a key technology that can solve this challenge. KAIST announced on the 24th that the research team led by Prof. Nam-Soon Choi from the Department of Chemical and Biomolecular Engineering and Prof. Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with Prof. Sang Kyu Kwak’s team at Korea University, has developed a technology that resolves the most critical challenge of lithium-metal batteries, “interfacial instability,” at the electronic structure level. Interfacial instability refers to the phenomenon in which the boundary between the electrode and electrolyte cannot be maintained uniformly during charging and discharging. As a result, lithium grows in needle-like dendrites, which leads to reduced battery cyclability, internal short circuits, and increased Thermal instability. This has been the fundamental cause preventing the commercialization of lithium-metal batteries. The research team implemented an “intelligent protective layer” that allows lithium ions to move stably along the electrode surface by adding thiophene to the battery electrolyte. This protective layer has the characteristic that its electronic structure rearranges itself. Like a smart traffic system that adjusts lanes according to traffic flow, the charge distribution inside the protective layer flexibly changes whenever lithium ions move, creating optimal pathways. The research team identified this mechanism through density functional theory (DFT) simulations and confirmed much higher stability compared to existing commercial additives. As a result, they succeeded in effectively suppressing dendrite growth even under fast-charging conditions and significantly extending battery lifespan. In addition, the research team directly observed the inside of the battery at the nanometer scale using in-situ atomic force microscopy (AFM). Even under high current conditions, they confirmed that lithium was deposited and removed uniformly on the surface, thereby verifying mechanical stability. This technology can be applied to various cathode materials currently widely used, including lithium iron phosphate (LiFePO₄), lithium cobalt oxide (LiCoO₂), and lithium nickel–cobalt–manganese oxide LiNixCoyMn1-x-yO₂). Because it is not limited to a specific battery type and can be broadly applied across existing electric vehicle battery systems, it is expected to have significant industrial impact. This achievement is meaningful in that it presents a breakthrough capable of fundamentally solving the ultra-fast charging problem—which has been the biggest barrier to lithium-metal battery commercialization—by simultaneously enabling fast charging within 12 minutes and high-current operation exceeding 8 mA/cm². 8 mA/cm² refers to a level at which 8 milliamperes of current flow per square centimeter of battery electrode area. In lithium-metal battery research, even around 4 mA/cm² is typically considered a “high current” condition, so this represents more than twice that level and corresponds to operating conditions close to real-world electric vehicle fast charging, rapid acceleration, and high-power driving. Through this breakthrough, the technology is expected to be applied to various future industries requiring high-performance batteries, including ultra-long-range electric vehicles, urban air mobility (UAM), and next-generation high-density energy storage systems. Prof. Nam-Soon Choi stated, “This research is not simply a material improvement but an achievement that solves the fundamental problem of batteries by designing the electronic structure,” adding, “It will become a core foundational technology for next-generation electric vehicle batteries that simultaneously achieve fast charging and long lifespan.” This study was conducted by Jeong-A. Lee, Haneul Kang, Yoonhan Cho, Seong Hyeon Kweon, Seonghyun Kim, Syed Azkar UI Hasan, Minju Song, Saehun Kim, Eunji Kwon, Samuel Seo, Kyoung Han Ryu, Rama K. Vasudevan, Sang Kyu Kwak, Seungbum Hong, and Nam-Soon Choi, and was published on February 2 in the internationally renowned materials and energy journal InfoMat. Paper title: Conjugation-mediated and polarity-switchable interfacial layers for fast cycling of lithium-metal batteries DOI: http://doi.org/10.1002/inf2.70126 Meanwhile, this research was conducted with support from Hyundai Motor Company and the mid-career researcher program of the National Research Foundation of Korea.

Developing Technology to Become the Joker in The D..

<(From left) Ph.D. candidate Taewoong Kang, Ph.D candidate Junha Hyung, Professor Jaegul Choo, and Ph.D. candidate Minho Park (From top right square, from left), Ph.D. candidate Kinam Kim, Seoul National University undergraduate researcher Dohyeon Kim> What if, while watching The Dark Knight, you weren't just observing the Joker on screen, but actually seeing Gotham City through his eyes? The video technology that allows viewers to experience the world through a character's perspective, rather than as a mere observer, is becoming a reality. Researchers at our university have developed a new AI model that generates first-person viewpoint videos from standard footage. KAIST announced on February 23rd that Professor Jaegul Choo’s research team at the Kim Jaechul Graduate School of AI has developed 'EgoX,' an AI model that utilizes observer-perspective (exocentric) video to precisely generate the scenes that a person in the video would actually be seeing. With the rapid advancement of Augmented Reality (AR), Virtual Reality (VR), and AI robotics, the importance of "egocentric video"—which captures scenes as one directly sees them—is growing. However, obtaining high-quality first-person footage previously required users to wear expensive action cameras or smart glasses. Furthermore, there were significant technical limitations in naturally converting existing standard (third-person or exocentric) video into a first-person perspective. A key feature of this technology is that it goes beyond simply rotating the screen; it comprehensively understands the person's position, posture, and the 3D structure of the surrounding space to reconstruct the first-person viewpoint. < Example of converting a third-person perspective video into a first-person perspective video > Existing technologies often only converted still images or required footage from four or more cameras. Additionally, they frequently suffered from awkward visual artifacts in videos with complex lighting or rapid movement. In contrast, EgoX can generate high-quality first-person video from just a single third-person video source. Specifically, the research team succeeded in realistically implementing natural shifts in vision—such as when a person turns their head—by precisely modeling the correlation between head movement and the actual field of view. This technology demonstrated stable performance across various daily scenarios, including cooking, exercising, and working, without being limited to specific environments. It is being evaluated as a breakthrough that opens new possibilities for securing high-quality first-person data from existing video archives without the need for wearable devices. EgoX is expected to have a significant impact across various industries. In the fields of AR, VR, and the Metaverse, it can maximize user experience by transforming standard videos into immersive content that makes users feel as if they are experiencing the scene firsthand. Furthermore, it is projected to contribute to the fields of robotics and AI training by serving as core data for "Imitation Learning," where robots learn by watching human actions. New types of video services, such as switching sports broadcasts or vlogs to the perspective of the athlete or the protagonist, are also anticipated. < EgoX technology that converts a third-person perspective into a first-person perspective (AI-generated image) > Distinguished Professor Jaegul Choo stated, "This research is significant in that AI has moved beyond simple video conversion to learning and reconstructing human 'vision' and 'spatial understanding.' We expect an environment to open up where anyone can create and experience immersive content using only previously recorded videos." He added, "KAIST will continue to secure global competitiveness in the field of generative AI-based video technology." This research was led by first authors Taewoong Kang, Kinam Kim, and Dohyeon Kim . The paper was pre-released on arXiv on December 9, 2025, garnering significant attention from AI industry giants like NVIDIA and Meta, as well as academia. It is scheduled for official presentation at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), an international academic conference to be held in Colorado, USA, on June 3, 2026. Paper Title: EgoX: Egocentric Video Generation from a Single Exocentric Video Paper Link: https://keh0t0.github.io/EgoX/ Meanwhile, this research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea's individual basic research project, "Research on User-Centered Content Generation and Editing Technology through Generative AI," and the Supercomputer No. 5 High-Performance Computing-based R&D Innovation Support project, "Research on Video Filming Viewpoint Conversion Based on Diffusion Models."

KAIST Overcomes Limitations of Existing Image Sens..

<(From Left) Ph.D candidate Chanhyung Park from Electrical Engineering, Jaehyun Jeon from Department of Physics, Professor Min Seok Jang from Electrical Engineering> Smartphone cameras are becoming smaller, yet photos are becoming sharper. Korean researchers have elevated the limits of next-generation smartphone cameras by developing a new image sensor technology that can accurately represent colors regardless of the angle at which light enters. The team achieved this by utilizing a “metamaterial” that designs the movement of light through structures too small to be seen with the naked eye. KAIST (President Kwang Hyung Lee) announced on the 12th of February that a research team led by Professor Min Seok Jang of the School of Electrical Engineering, in collaboration with Professor Haejun Chung’s team at Hanyang, has developed a metamaterial-based technology for image sensors that can stably separate colors even when the angle of light incidence varies. Conventional smartphone cameras capture images by concentrating light into a small lens. However, as camera pixels become extremely small, lenses alone struggle to gather sufficient light. To address this, the Nanophotonic Color Router was introduced. Instead of concentrating light through a lens, this technology uses microscopic structures invisible to the eye to precisely separate incoming light by color. By designing the pathways through which light travels, this metamaterial-based structure accurately divides light into red (R), green (G), and blue (B). Samsung Electronics has already demonstrated the commercialization potential of this technology by applying it to actual image sensors under the name “Nano Prism.” Theoretically, stacking multiple layers of extremely fine nanostructures enables greater light collection and more accurate color separation. <Nanophotonic color router technology that works reliably even under oblique incidence conditions (AI-generated image)> However, existing Nanophotonic Color Routers had limitations. While they functioned well when light entered vertically, their performance deteriorated significantly—or colors mixed—when light entered at an angle, as is common in smartphone cameras. This issue, known as the “oblique incidence problem,” has been considered a critical challenge that must be resolved for real-world product applications. The research team first investigated the root cause of this issue. They found that previous designs were overly optimized for vertically incident light, causing performance to drop sharply even with slight changes in the angle of incidence. Since smartphone cameras receive light from various angles, maintaining performance under angular variation is essential. Instead of manually designing the structure, the team adopted an “inverse design” approach, which allows the computer to autonomously determine the optimal structure. Through this method, they derived a color router design capable of stable color separation even when the angle of incoming light changes. As a result, whereas previous structures nearly failed when light was tilted by about 12 degrees, the newly designed structure maintained approximately 78％ optical efficiency within a ±12-degree range, demonstrating stable color separation performance. In other words, the technology reaches a level suitable for practical smartphone usage environments. <Nanophotonic color router robust to oblique incidence> The team further analyzed performance variations by considering factors such as the number of metamaterial layers, design conditions, and potential fabrication errors. They also systematically defined the limits of robustness against changes in the angle of incidence. This study is particularly meaningful in that it presents design criteria for color routers that reflect realistic image sensor environments. Professor Min Seok Jang of KAIST stated, “This research is significant in that it systematically analyzes the oblique incidence problem, which has hindered the commercialization of color router technology, and proposes a clear solution direction,” adding, “The proposed design methodology can be extended beyond color routers to a wide range of metamaterial-based nanophotonic devices.” In this study, KAIST undergraduate student Jaehyun Jeon and doctoral candidate Chanhyung Park participated as co-first authors. The research findings were published on January 27 in the international journal Advanced Optical Materials. ※ Paper title: “Inverse Design of Nanophotonic Color Router Robust to Oblique Incidence” DOI: https://doi.org/10.1002/adom.202501697 ※ Authors: Jaehyun Jeon (KAIST, first author), Chanhyung Park (KAIST, first author), Doyoung Heo (KAIST), Haejun Chung (Hanyang University), Min Seok Jang (KAIST, corresponding author) This research was supported by the Ministry of Trade, Industry & Energy (Korea Institute for Advancement of Technology, Korea Semiconductor Research Consortium) under the project “Design Technology of Meta-Optical Structures for Next-Generation Sensors,” by the Ministry of Science and ICT (National Research Foundation of Korea) under the projects “Development of Full-Color Micro LED Devices and Panels Based on Beam-Steerable High-Color-Purity Meta Color Conversion Layers” and “Development of a Real-Time Zero-Energy Argos-Eye Metasurface Network Computing with All Properties of Light,” and by the Ministry of Culture, Sports and Tourism (Korea Creative Content Agency) under the project “International Joint Research for Next-Generation Copyright Protection and Secure Content Distribution Technologies.”

KAIST Uses Sandpaper to Polish Semiconductors… Ope..

<(From Left) Dr. Sukkyung Kang, Professor Sanha Kim from Department of Mechanical Engineering> The performance and stability of smartphones and artificial intelligence (AI) services depend on how uniformly and precisely semiconductor surfaces are processed. KAIST researchers have expanded the concept of everyday “sandpaper” into the realm of nanotechnology, developing a new technique capable of processing semiconductor surfaces uniformly down to the atomic level. This technology demonstrates the potential to significantly improve surface quality and processing precision in advanced semiconductor processes such as high-bandwidth memory (HBM). KAIST (President Kwang Hyung Lee) announced on the 11th of February that a research team led by Professor Sanha Kim of the Department of Mechanical Engineering has developed a “nano sandpaper” that utilizes carbon nanotubes—tens of thousands of times thinner than a human hair—as abrasive materials. This technology enables more precise surface processing than existing semiconductor manufacturing processes, while also reducing environmental burdens generated during fabrication, presenting a new planarization technique. < Nano Sandpaper AI-Generated Image > Although sandpaper is a familiar tool used to smooth surfaces by rubbing, it has been difficult to apply it to fields such as semiconductors, where extremely precise surface processing is required. This limitation arises because conventional sandpaper is manufactured by attaching abrasive particles with adhesives, making it difficult to uniformly secure extremely fine particles. To overcome such limitations, the semiconductor industry has adopted a planarization process known as chemical mechanical polishing (CMP), which uses a chemical slurry in which abrasive particles are dispersed in liquid. However, this method requires additional cleaning steps and generates large amounts of waste, making the process complex and environmentally burdensome. To address these issues, the research team extended the concept of sandpaper to the nanoscale. By vertically aligning carbon nanotubes, fixing them inside polyurethane, and partially exposing them on the surface, they implemented a “nano sandpaper.” This structure structurally suppresses abrasive detachment, eliminating concerns about surface damage and maintaining stable performance even after repeated use. The nano sandpaper developed in this study achieves an abrasive density approximately 500,000 times higher than that of the finest commercially available sandpaper. The precision of sandpaper is expressed in terms of “abrasive density (grit number),” which indicates how densely abrasive particles are arranged on the surface. While everyday sandpaper typically ranges from 40 to 3000 grit, the nano sandpaper exceeds 1,000,000,000 grit. Through this extremely dense structure, surfaces could be processed with precision down to several nanometers—equivalent to the thickness of only a few atoms. The effectiveness of the nano sandpaper was confirmed through experiments. Rough copper surfaces were polished to a smoothness at the nanometer level, and in semiconductor pattern planarization experiments, the technique reduced dishing defects by up to 67％ compared with conventional CMP processes. Dishing defects refer to the phenomenon in which the center of interconnect lines becomes recessed, a major defect affecting the performance and reliability of advanced semiconductors such as HBM. In particular, because the abrasive materials are fixed on the sandpaper surface, the technology does not require continuous supply of slurry solutions as in conventional processes. This reduces cleaning steps and eliminates waste slurry, presenting the possibility of transitioning semiconductor manufacturing toward more environmentally friendly processes. < Nano Sandpaper Schematic Diagram > < Detailed Image of Nano Sandpaper > The research team expects that this technology can be applied to advanced semiconductor planarization processes such as HBM used in AI servers, as well as to hybrid bonding processes, which are gaining attention as next-generation semiconductor interconnection technologies. The study is also significant in that it expands the everyday concept of sandpaper into nano-precision processing technology, suggesting the possibility of securing core technologies required for semiconductor manufacturing. Professor Sanha Kim stated, “This is an original study demonstrating that the everyday concept of sandpaper can be extended to the nanoscale and applied to ultra-fine semiconductor manufacturing,” adding, “We hope this technology will lead not only to improved semiconductor performance but also to environmentally friendly manufacturing processes.” In this study, Dr. Sukkyung Kang of the Department of Mechanical Engineering participated as the first author. The research was recognized for its excellence by receiving the Gold Prize (1st place) in the Mechanical Engineering Division at the 31st Samsung Human Tech Paper Award, hosted by Samsung Electronics. The findings were published online on January 8, 2026, in the international journal Advanced Composites and Hybrid Materials (IF 21.8). ※ Paper title: “Carbon nanotube sandpaper for atomic-precision surface finishing” DOI: https://doi.org/10.1007/s42114-025-01608-3 This research was supported by the National Research Foundation of Korea (Mid-Career Researcher Program; Ministry of Science and ICT, NRF, RS-2025-00560856), the Glocal Lab Program (Ministry of Education, NRF, RS-2025-25406725), the InnoCORE Program (Ministry of Science and ICT, NRF, N10250154), and the KAIST Up Program.

Capturing the Instant of Electrical Switching, Pav..

< (From left) Ph.D candidate Changhwan Kim, Ph.D candidate Seunghwan Kim , Ph.D candidate Namwook Hur, Professor Joonki Suh, Ph. D candidate Youngseok Cho> As artificial intelligence advances, computers demand faster and more efficient memory. The key to ultra-high-speed, low-power semiconductors lies in the "switching" principle—the mechanism by which memory materials turn electricity on and off. A South Korean research team has successfully captured the elusive moment of switching and its internal operational principles by momentarily melting and freezing materials within a microscopic electronic device. This study provides a foundational blueprint for designing next-generation memory materials that are faster and consume less power based on fundamental principles. On February 8th, the research team led by Professor Joonki Suh from our department (Chemical and Biomolecular Engineering), in collaboration with Professor Tae-Hoon Lee’s team from Kyungpook National University, announced the development of an experimental technique capable of real-time monitoring of electrical switching processes and phase changes within nano-devices—phenomena that were previously difficult to observe. To verify the electrical switching, the team applied a method of instantaneous melting followed by rapid cooling (quenching). Through this, they succeeded in stably implementing amorphous tellurium (a-Te)—a state where tellurium is disordered like glass—within a nano-device much smaller than a human hair. Tellurium is typically sensitive to heat and changes properties easily when current is applied; however, in its amorphous state, it is garnering significant attention as a core material for next-generation memory due to its speed and energy efficiency. ＊Tellurium (Te): A metalloid element possessing properties of both metals and non-metals. < Illustration of the experiment involving instantaneous melting and freezing in a memory electronic device (AI-generated image) > Through this study, the team specifically identified the threshold voltage and thermal conditions at which switching begins, as well as the segments where energy loss occurs. Based on these findings, they observed stable and high-speed switching even while reducing heat generation. This enables "principle-based" memory material design, allowing researchers to understand exactly why and when electricity starts to flow. The results confirmed that microscopic defects within amorphous tellurium play a crucial role in electrical conduction. When the voltage exceeds a certain threshold, the electricity does not flow all at once; instead, it follows a two-step switching process: first, a rapid increase in current along the defects, followed by heat accumulation that causes the material to melt. Furthermore, the team successfully implemented a "self-oscillation" phenomenon—where voltage spontaneously increases and decreases—by conducting experiments that maintained the amorphous state without excessive current flow. This demonstrates that stable electrical switching is possible using only the single element of tellurium, without the need for complex material combinations. < Electrical characteristics of amorphous tellurium created through rapid cooling from a liquid state within an electronic device > This research is a significant achievement as it implements amorphous tellurium—a next-generation memory material—within an actual electronic device and systematically elucidates the fundamental principles of electrical switching. These findings are expected to serve as essential guidelines for designing semiconductor materials to realize faster and more energy-efficient memory in the future. "This is the first study to implement amorphous tellurium in a real-world device environment and clarify the switching mechanism," said Professor Joonki Suh. "It sets a new standard for research into next-generation memory and switching materials." The study, with Namwook Hur as the first author and Seunghwan Kim as the second author, and Professor Joonki Suh (KAIST) as the corresponding author, was published online on January 13th in the international academic journal Nature Communications. Paper Title: On-device cryogenic quenching enables robust amorphous tellurium for threshold switching DOI: 10.1038/s41467-025-68223-0 Meanwhile, this research was supported by the National Research Foundation of Korea (NRF) through the PIM (Processor-in-Memory) AI Semiconductor Core Technology Development Project, the Excellent Young Researcher Program funded by the Ministry of Science and ICT, and Samsung Electronics.

Unveiling the Oxygen Usage of Catalysts to Elimina..

<(From Left) Professor Hyunjoo Lee, Ph. D candidate Yunji Choi, Ph. D candidate Jaebeom Han, Professor Jeong Young Park> As the climate crisis becomes a part of daily life with unprecedented heatwaves and cold snaps, technology to effectively remove greenhouse gases is emerging as a critical global challenge. In particular, catalytic technology that decomposes harmful gases using oxygen is a key element of eco-friendly purification. South Korean researchers have identified the principle that catalysts—which were previously vaguely thought to simply ‘use oxygen well’—can selectively utilize different oxygen sources depending on the reaction environment, presenting a new standard for catalyst design. A joint research team consisting of Professor Hyunjoo Lee from KAIST Department of Chemical and Biomolecular Engineering, Professor Jeong Woo Han from Seoul National University, and Professor Jeong Young Park from KAIST announced on February 4th that they have identified for the first time in the world that ceria (CeO₂), widely used as an eco-friendly catalyst, completely changes its method of using oxygen depending on its size. ＊Ceria (CeO₂): A compound formed by the combination of the metal cerium and oxygen. Ceria is a metal oxide catalyst enables high catalytic performance while reducing the need for expensive precious metal catalysts. It is called an ‘oxygen tank’ in the field of catalysis because it can store oxygen and release it when needed. However, until now, it had not been clearly identified where the oxygen came from and under what conditions it was used in the reaction. The research team focused on a new concept of a catalyst that ‘chooses and uses oxygen according to the situation,’ rather than just a catalyst that ‘uses oxygen well.’ To this end, they fabricated catalysts with precisely controlled ceria sizes, ranging from ultra-small nano-sizes to relatively large sizes, and systematically analyzed the oxygen movement and reaction processes. <Schematic Diagram of the Oxygen Transport Mechanism According to Seria Size> As a result, it was confirmed that small ceria catalysts operate as an ‘agility type’ that quickly takes in oxygen from the air and uses it immediately for reactions, while large ceria catalysts play an ‘endurance type’ role that pulls oxygen stored inside to the surface and supplies it continuously. In other words, the design principle was revealed for the first time that by simply adjusting the size of the catalyst, one can choose whether to use oxygen from the air or oxygen stored internally depending on the reaction conditions. The research team proved this mechanism simultaneously through advanced experimental analysis and artificial intelligence-based simulations. The research team applied this principle to methane removal. Methane is a greenhouse gas with a global warming effect dozens of times stronger than carbon dioxide, and it is removed through a catalytic oxidation reaction that converts it into carbon dioxide and water using oxygen. The experimental results showed that the small ceria catalyst, by immediately utilizing oxygen from the air, demonstrated stable performance in removing methane even in low-temperature and high-humidity environments. This shows that it is possible to significantly reduce the use of expensive precious metals (platinum and palladium) while actually improving performance. This achievement is expected to lead to the development of highly durable catalysts that maintain performance even in realistic industrial environments such as rain and moisture, as well as reducing the manufacturing cost of environmental purification equipment, thereby accelerating the commercialization of eco-friendly energy and environmental technologies. <Schematic Illustration of Ceria Catalyst Applications> Professor Hyunjoo Lee stated, “This research is an achievement that clearly distinguishes the two core mechanisms of how oxygen operates in catalysts for the first time,” and added, “It has opened a new path to custom-design high-efficiency catalysts required for responding to the climate crisis according to reaction conditions.” Ph. D candidate Yunji Choi from KAIST, Dr. Seokhyun Choung from Seoul National University, and Ph. D candidate Jaebeom Han from KAIST participated as joint first authors of this study. The research results, also co-authored by Jae-eon Hwang, Hyeon Jin, Yunkyung Kim, and Jeongjin Kim, were published in the international academic journal 'Nature Communications' on January 9th. This research was supported by the National Research Foundation of Korea (Global Leader Grant, Mid-Career Research Program) funded by the Ministry of Education, Science and Technology, Republic of Korea.

KAIST Develops Cap-Like OLED Wearable to Prevent H..

<Professor Kyung Cheol Choi, (Upper Left) Dr. Eun Hae Cho> A new solution that could overcome the limitations of conventional hair-loss treatments is emerging. Heavy and rigid helmet-type phototherapy devices may soon become a thing of the past. A joint research team has developed a hat-like, wearable OLED-based phototherapy device and demonstrated that it can suppress hair-follicle cell aging by up to 92％, a key factor in hair-loss progression. KAIST (President Kwang Hyung Lee) announced on the 1st of February that a research team led by Professor Kyung Cheol Choi of the School of Electrical Engineering, in collaboration with Professor Yun Chi’s group at the Hong Kong University of Science and Technology, has developed a non-invasive＊ hair-loss treatment technology using a textile-like, flexible wearable platform integrated with specially designed OLED light sources. ＊Non-invasive treatment refers to therapies that do not involve skin incisions or direct physical damage to the body. Although drug-based treatments for hair loss have been known to be effective, concerns over side effects from long-term use have driven interest in safer alternatives such as phototherapy. However, existing phototherapy devices for hair loss are typically bulky, rigid helmet-type systems, limiting their use to indoor environments. Moreover, because they rely on point light sources such as LEDs or lasers, it has been difficult to deliver uniform light irradiation across the entire scalp. To address these challenges, the researchers replaced point light sources with area-emitting OLEDs, which emit light uniformly over a wide surface. In particular, they integrated near-infrared (NIR) OLEDs into a soft, fabric-like material that can be worn as a cap. This design allows the light source to naturally conform to the contours of the scalp, delivering even optical stimulation over the entire scalp. Beyond wearable design, the study focused on suppressing hair-follicle cell aging, a central driver of hair-loss progression. The key achievement of this work lies not only in realizing a wearable device, but also in precisely tailoring the wavelength of light to maximize therapeutic efficacy. Recognizing that cellular responses vary depending on light wavelength, the team extended wavelength-control techniques originally developed for display OLEDs to therapeutic applications. As a result, they fabricated customized OLEDs that selectively emit near-infrared light in the 730–740 nm range, which is optimal for activating dermal papilla cells—critical cells located at the base of hair follicles that regulate hair growth. The effectiveness of the developed NIR OLEDs was validated through experiments using human dermal papilla cells (hDPCs). Cellular aging analysis showed that NIR OLED irradiation suppressed cell aging by approximately 92％ compared with the control group, outperforming conventional red-light irradiation conditions. < external_image > <Schematic diagram of phototherapy using a textile-based near-infrared OLED cap> First author Dr. Eun Hae Cho commented, “Instead of rigid, helmet-type point-light devices, we propose a wearable phototherapy platform that can be used in daily life by implementing soft, textile-based OLEDs in a cap form. A key outcome of this study is demonstrating that precisely engineered light wavelengths can effectively suppress hair-follicle cell aging.” Professor Kyung Cheol Choi added, “Because OLEDs are thin and flexible, they can closely conform to the curved surface of the scalp, delivering uniform light stimulation across the entire area. Going forward, we plan to verify safety and efficacy through preclinical studies and progressively evaluate the potential for real therapeutic applications.” This research was led by Dr. Eun Hae Cho of the KAIST School of Electrical Engineering as first author and was published online on January 10 in the international journal Nature Communications. ※ Paper title: “Wearable Textile-Based Phototherapy Platform With Customized NIR OLEDs Toward Non-Invasive Hair Loss Treatment", DOI: https://doi.org/10.1038/s41467-025-68258-3, Co-authors: Eun Hae Cho, Jingi An, Yun Chi, Kyung Cheol Choi <Prototype of a textile-based near-infrared OLED and its phototherapy efficacy> This research was conducted with the support of the Ministry of Science and ICT through the National Research Foundation of Korea (NRF) under the National R&D Program (Future-Oriented R&D Convergence Science and Technology Development Program (Bridge Convergence Research): Development of a skin patch for wound treatment integrating bio-tissue adhesive patches with drug delivery and phototherapy OLED therapy, the Technology Innovation Program supported by the Ministry of Trade, Industry and Energy (development of substrate materials stretchable by more than 50％ for stretchable displays), and the BK21 FOUR Program of the Ministry of Science and ICT (Connected AI Education & Research Program for Industry and Society Innovation, School of Electrical Engineering, KAIST). (2021M3C1C3097646, 20017569, 4120200113769)

KAIST Team Wins Grand Prize at Kakao AI Incubation..

<(From Left) Professor Jongse Park, Professor youngjin Kwon, Professor Jaehyuk Huh, Professor Knunle Olukotun> Currently, Large Language Model (LLM) services like ChatGPT rely heavily on expensive GPU servers. This structure faces significant limitations, as costs and power consumption skyrocket as service scales increase. Researchers at KAIST have developed a next-generation AI infrastructure technology to address these challenges. KAIST announced on January 30th that the ‘AnyBridge AI’ team, led by Professor Jongse Park from the School of Computing, has developed a next-generation AI infrastructure software. This software allows for efficient LLM services by integrating various AI accelerators instead of relying solely on GPUs. The technology won the Grand Prize at the "4 ISTs (Science & Tech Institutes) × Kakao AI Incubation Project" hosted by Kakao. This project is a joint industry-academic collaboration between Kakao and the four major science and technology institutes (KAIST, GIST, DGIST, and UNIST). It selected outstanding teams by evaluating the technical prowess and business viability of preliminary startup teams based on AI technology. The Grand Prize winning team receives a total of 20 million KRW in prize money and up to 35 million KRW in Kakao Cloud credits. AnyBridge AI is a technical startup team led by Professor Jongse Park (CEO), with Professors Youngjin Kwon and Jaehyuk Huh from KAIST's School of Computing participating. Based on research achievements in AI systems and computer architecture, the team aims to develop technology applicable to actual industrial sites. Furthermore, Professor Kunle Olukotun of Stanford University—co-founder of the Silicon Valley AI semiconductor startup SambaNova—is participating as an advisor to push for global technology and business expansion. The AnyBridge team noted that most current LLM services are dependent on expensive GPU infrastructure, leading to structural limits where operating costs and power usage surge as services scale. The researchers analyzed that the root cause of this issue lies not in the performance of specific hardware, but in the absence of a system software layer capable of efficiently connecting and operating various AI accelerators, such as NPUs (AI-specialized chips) and PIMs (next-gen chips that process AI within memory), alongside GPUs. <Technical diagram of AnyBridge: Enhancing LLM performance by flexibly utilizing various AI accelerators> In response, the AnyBridge team proposed an integrated software stack that can service LLMs across the same interface and runtime environment, regardless of the accelerator type. Specifically, they received high praise for pointing out the limitations of existing GPU-centric LLM serving structures and presenting a "Multi-Accelerator LLM Serving Runtime Software" as their core technology. This technology enables the implementation of a flexible AI infrastructure where the most suitable AI accelerator can be selected and combined based on the task's characteristics, without being tied to a specific vendor or hardware. This is evaluated as a major advantage that can reduce costs and power consumption while significantly increasing scalability for LLM services. <Illustration of the Multi-Accelerator LLM Service Platform - AI-generated image> Additionally, based on years of accumulated research in LLM serving system simulation, the AnyBridge team possesses a research foundation that can pre-verify various hardware/software design combinations without building a large-scale physical infrastructure. This point demonstrated both the technical maturity and the industrial feasibility of their work. "This award is a result of recognizing the necessity of system software that integrates various AI accelerators, moving beyond the limits of GPU-centric AI infrastructure," said Professor Jongse Park. He added, "It is meaningful that we could expand our research results into industrial fields and entrepreneurship. We will continue to develop this into a core technology for next-generation LLM serving infrastructure through cooperation with industrial partners." This award is seen as a prime example of KAIST's research moving beyond academic papers toward next-generation AI infrastructure technology and startups. AnyBridge AI plans to advance and verify its technology through future collaborations with Kakao and related industrial partners. <Photo of the Grand Prize ceremony: Left - Kakao Investment CEO Do-young Kim; Right - KAIST Prof. Jongse Park>

KAIST Solves Key Micro-LED Challenges, Enabling Re..

<(Back row, from left) Dr. Juhyuk Park, Ph.D candidate Hyunsu Ki, (Front row, from left) M.S candidate Haoi Le Bao, M.S candidate Chaeyeon Kim, (Circled, from left) Prof. Sanghyeon Kim, Prof. Dae-Myeong Keum > From TVs and smartwatches to rapidly emerging VR and AR devices, micro-LEDs are a next-generation display technology in which each LED—smaller than the thickness of a human hair—emits light on its own. Among the three primary colors required for full-color displays—red, green, and blue—the realization of high-performance red micro-LEDs has long been considered the most difficult. KAIST researchers have now successfully demonstrated a high-efficiency, ultra-high-resolution red micro-LED display, paving the way for displays that can deliver visuals even sharper than reality. KAIST (President Kwang Hyung Lee) announced on the 28th that a research team led by Professor Sanghyeon Kim of the School of Electrical Engineering, in collaboration with Professor Dae-Myeong Geum of Inha University, compound-semiconductor manufacturer QSI, and microdisplay/SoC design company Raontech, has developed a red micro-LED display technology that achieves ultra-high resolution while significantly reducing power consumption. Using this technology, the team successfully demonstrated a 1,700 PPI＊ class ultra-high-resolution micro-LED display—approximately 3–4 times higher than the resolution of current flagship smartphone displays—capable of delivering truly “reality-like” visuals even in VR and AR devices. ＊PPI (Pixels Per Inch): indicates how densely pixels are packed on a display; higher PPI corresponds to finer image detail. Micro-LEDs are self-emissive displays that surpass OLEDs in brightness, lifetime, and energy efficiency, but they have faced two major technical challenges. The first is the efficiency degradation of red micro-LEDs, which becomes severe as pixel sizes shrink due to increased energy leakage. The second is the limitation of conventional transfer processes, which rely on mechanically locating and placing countless microscopic LEDs one by one, making ultra-high-resolution fabrication difficult and increasing defect rates. <Results of Red Micro-LED Performance Improvement> The research team addressed both challenges simultaneously. First, they adopted an AlInP/GaInP quantum-well structure, enabling highly efficient red micro-LEDs with minimal energy loss even at very small pixel sizes. Simply put, the quantum well/barrier structure acts as an “energy barrier.” It confines electrons and holes within the quantum well layer, preventing carrier leakage. By adopting quantum wells with higher hole concentration, the research team effectively reduced energy loss as pixel sizes decreased, enabling brighter and more efficient red micro-LEDs. Also, instead of transferring individual LEDs, the researchers employed a monolithic three-dimensional (3D) integration technique, stacking the LED layers directly on top of the driving circuitry. This approach minimizes alignment errors, reduces defect rates, and enables stable fabrication of ultra-high-resolution displays. The team also developed a low-temperature process to prevent damage to the underlying circuitry during integration. <Monolithic 3D MicroLED-on-Si Display> This achievement is particularly significant because it demonstrates a fully functional, ultra-high-resolution, and highly-quantum-efficient red micro-LED display, widely regarded as the most difficult component to realize. The technology is expected to find broad applications in next-generation displays where pixel granularity must be virtually imperceptible, including AR/VR smart glasses, automotive head-up displays (HUDs), and ultra-compact wearable devices. Professor Sanghyeon Kim commented, “This work simultaneously solves the long-standing challenges of red pixel efficiency and circuit integration in micro-LEDs. We will continue to advance this technology toward practical commercialization as a next-generation display platform.” The study was led by Dr. Juhyuk Park of the KAIST Institute of Information Electronics as first author, and the results were published on January 20 in the international journal Nature Electronics. ※ Paper title: “A Monolithic Three-Dimensional Integrated Red Micro-LED Display on Silicon Using AlInP/GaInP Epilayers” ※ DOI: 10.1038/s41928-025-01546-4 This research was supported by the National Research Foundation of Korea Basic Research Program (2019), the Display Strategic Research Laboratory Program (currently ongoing), and the Samsung Future Technology Incubation Center (2020-2023). <Monolithic 3D Direct Technology (AI-generated image)>

AI Enters the Experienced Hire Era... Teaching Lea..

< (From left) KAIST Professor Hyunwoo J. Kim, Postdoctoral Researcher Sanghyeok Lee, M.S candidate Taehoon Song, Korea University Ph.D candidate Jihwan Park > How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current Artificial Intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. A Korean research team has developed a "knowledge transplantation" technology between AI models that can resolve this inefficiency. KAIST announced on January 27th that a research team led by Professor Hyunwoo J. Kim from the School of Computing, in collaboration with a research team from Korea University, has developed a new technology capable of effectively "transplanting" learned knowledge between different AI models. Recently, Vision-Language Models (VLM), which understand both images and text simultaneously, have been evolving rapidly. These are easily understood as multimodal AIs, like ChatGPT, which can provide explanations when a user shows them a photo and asks a question. These models have the advantage of adapting relatively quickly to new fields using small amounts of data by pre-learning large-scale image and language data. However, the need to repeat this "adaptation process" from scratch every time a new AI model is released has been pointed out as a major inefficiency. Existing adaptation techniques also faced limitations: they were difficult to use if the model structure changed even slightly, or they significantly increased memory and computational costs because multiple models had to be used simultaneously. To solve these problems, the research team proposed "TransMiter," a transferable adaptation technique that allows learned knowledge to be reused regardless of the model's structure or size. The core of this technology is directly transferring the "adaptation experience" accumulated by one AI as it learns to another AI model. < TransMiter: A transferable adaptation technique reusable regardless of model structure, size, etc. > The researchers' technology does not overhaul the complex internal structure of the AI; instead, it adopts a method of passing on "know-how" learned by observing only the prediction results (output) to another AI. Even if the AI models have different architectures, if the know-how learned by one AI is organized based on the answers given to the same questions, another AI can utilize that knowledge immediately. Consequently, there is no need to undergo the complex and time-consuming retraining process, and there is almost no slowdown in speed. This study is highly significant as it is the first to prove that AI adaptation knowledge—previously considered almost impossible to reuse if model structures or sizes differed—can be precisely transplanted regardless of the model type. This is expected to not only reduce repetitive learning costs but also be utilized as a so-called "knowledge patch" technology that updates Large Language Models (LLMs) in real-time according to specific needs. Professor Hyunwoo J. Kim explained, "By extending this research, we can significantly reduce the cost of post-training that had to be performed repeatedly whenever a rapidly evolving hyper-scale language model appears. It will enable 'model patches' that easily add expertise in specific fields." The study involved Taehoon Song (Master's student, KAIST School of Computing), Sanghyeok Lee (Postdoctoral researcher), and Jihwan Park (Doctoral student, Korea University) as co-authors, with Professor Hyunwoo J. Kim serving as the corresponding author. The research results were accepted for oral presentation (4.6％ acceptance rate as of 2025) at AAAI 2026 (Association for the Advancement of Artificial Intelligence), the most prestigious international conference in the field of AI, and were presented on January 25th. Paper Title: Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization DOI: https://doi.org/10.48550/arXiv.2508.08604 Meanwhile, Professor Hyunwoo J. Kim's laboratory presented a total of three papers at the conference, including this paper and "TabFlash," a technology developed in collaboration with Google Cloud AI to enhance the understanding of tables within documents.

KAIST’s Reliability-Aware AI Opens Path to Faster ..

< (From front left) Professor Seungbum Hong, Professor EunAe Cho (From back left) Chaeyul Kang, Benediktus Madika, Jung Hyeon Moon, Taemin Park (Top) JooSung Shim > The power that makes electric vehicles travel further and smartphones last longer comes from battery materials. Among them, the core material that directly determines the performance and lifespan of a battery is the cathode material. What if artificial intelligence could replace the numerous experiments required for battery material development? KAIST's research team has developed an artificial intelligence (AI) framework that presents both the particle size of cathode materials and prediction reliability even in situations where experimental data is insufficient, opening the possibility of expansion to next-generation energy technologies such as all-solid-state batteries. KAIST announced on January 26th that a research team led by Professor Seungbum Hong of the Department of Materials Science and Engineering, in joint research with Professor EunAe Cho's team, has developed a machine learning framework that accurately predicts the particle size of battery cathode materials even when experimental data is incomplete and provides the degree of reliability of the results. The cathode material inside the battery is the core material that allows lithium-ion batteries to store and use energy. Currently, the most widely used cathode material for electric vehicle batteries is an NCM-based metal oxide mixed with nickel (Ni), cobalt (Co), and manganese (Mn), which greatly affects the battery's lifespan, charging speed, driving range, and safety. KAIST research team focused on the fact that the size of the very small primary particles that make up these cathode materials is a key factor in determining battery performance. This is because if the particles are too large, performance deteriorates, and conversely, if they are too small, stability problems may occur. Accordingly, the research team developed an AI-based technology that can accurately predict and control particle size. < Battery performance prediction related (AI-generated image) > In the past, to determine the particle size, numerous experiments had to be repeated while changing the sintering temperature, time, and material composition. However, in actual research fields, it was difficult to measure all conditions without omission, and experimental data were often missing, which limited the precise analysis of the relationship between process conditions and particle size. To solve this problem, the research team designed an AI framework that supplements missing data and presents prediction results along with reliability. This framework is characterized by combining a technology (MatImpute) that supplements missing experimental data by considering chemical characteristics and a probabilistic machine learning model (NGBoost) that calculates prediction uncertainty. This AI model does not stop at simply predicting particle size but also provides information on the extent to which the prediction can be trusted. This serves as an important criterion for deciding under what conditions to actually synthesize materials. As a result of learning by expanding experimental data, the AI model showed a high prediction accuracy of about 86.6％. According to the analysis, it was found that the cathode material particle size is more significantly affected by process conditions such as baking temperature and time than by material components, which aligns well with existing experimental understanding. To verify the reliability of the AI prediction, the research team conducted an experiment by newly producing four types of cathode material samples synthesized under manufacturing conditions not included in the existing data while maintaining the same metal component ratio of NCM811 (Ni 80％ / Co 10％ / Mn 10％) composition. As a result, the particle size predicted by the AI almost matched the actual microscopic measurement results, and most of the errors were 0.13 micrometers (μm) or less, which is much smaller than the thickness of a human hair. In particular, the actual experimental results were included within the prediction uncertainty range presented by the AI, confirming that not only the predicted value but also its reliability was valid. < Distribution shift condition experiment verification using 4 types of samples > This study is significant in that it has opened a way to find conditions with a high probability of success first without performing all experiments in battery research. Through this, it is expected to speed up the development of battery materials and significantly reduce unnecessary experiments and costs. Professor Seungbum Hong said, "The key is that the AI presents not only the predicted value but also how much the result can be trusted," and added, "It will be of practical help in designing next-generation battery materials more quickly and efficiently." In this study, Benediktus Madika, a doctoral student in the Department of Materials Science and Engineering, participated as the first author, and it was published on October 8, 2025, in 'Advanced Science', an internationally prestigious academic journal in the field of materials science and chemical engineering. ※ Paper Title: Uncertainty-Quantified Primary Particle Size Prediction in Li-Rich NCM Materials via Machine Learning and Chemistry-Aware Imputation, DOI: https://doi.org/10.1002/advs.202515694 Meanwhile, this research was conducted by researchers Benediktus Madika, Chaeyul Kang, JooSung Shim, Taemin Park, Jung Hyeon Moon, and the research team of Professor EunAe Cho and Professor Seungbum Hong, and was conducted with support from the Ministry of Science and ICT (MSIT) National Research Foundation of Korea (NRF) Future Convergence Technology Pioneer (Strategic) (Project No. RS-2023-00247245). < Battery performance prediction (AI-generated image) >

KAIST Transforms Hydrogen Energy by Flattening Gra..

<(From Left) Ph.D candidate HyunWoo J Yang, Ph.D candidate SangJae Lee, Professor EunAe Cho, Ph.D candidate DongWon Shin> Catalysts are the “invisible engines” of hydrogen energy, governing both hydrogen production and electricity generation. Conventional catalysts are typically fabricated in granular particle form, which is easy to synthesize but suffers from inefficient use of precious metals and limited durability. KAIST researchers have introduced a paper-thin sheet architecture in place of granules, demonstrating that a structural innovation—rather than new materials—can simultaneously reduce precious-metal usage while enhancing both hydrogen production and fuel-cell performance. KAIST (President Kwang Hyung Lee) announced on the 21st of January that a research team led by Professor EunAe Cho of the Department of Materials Science and Engineering has developed a new catalyst architecture that dramatically reduces the amount of expensive precious metals required while simultaneously improving hydrogen production and fuel-cell performance. The core of this research lies in the application of ultrathin nanosheet structures, with thicknesses tens of thousands of times thinner than a human hair, enabling the team to overcome both efficiency and durability limitations of conventional catalysts. Water electrolyzers and fuel cells are key technologies for hydrogen energy production and utilization. However, their commercialization has been severely constrained by the scarcity and high cost of iridium (Ir) and platinum (Pt), which are commonly used as catalysts. In conventional particle-based catalysts, only a limited surface area participates in reactions, and long-term operation inevitably leads to performance degradation. To address this, the research team transformed agglomerated catalyst particles into paper-like, ultrathin and laterally extended sheets. For water electrolysis, they developed ultrathin iridium nanosheets with lateral size of 1–3 micrometers and thicknesses below 2 nanometers. This structure dramatically increased the active surface area participating in reactions, enabling significantly higher hydrogen production with the same amount of iridium. < Ultrafine Iridium Nanosheet (AI-generated image) > In addition, the team discovered that these ultrathin nanosheets naturally formed interconnected conductive pathways on titanium oxide (TiO2), a material previously considered unsuitable as a catalyst support due to its poor electrical conductivity. As a result, titanium oxide could be stably used as a catalyst support, further enhancing durability. The resulting catalyst achieved a 38％ higher hydrogen production rate than commercial catalysts and operated stably for over 1,000 hours under high-load, industry-relevant conditions (1 A/cm2＊). Notably, even with approximately 65％ less iridium, the catalyst delivered performance comparable to commercial benchmarks, demonstrating a major reduction in precious-metal usage. ＊1 A/cm2: a high-current condition corresponding to intensive operation of practical hydrogen-production systems The team further applied the ultrathin nanosheet design strategy to fuel-cell catalysts, producing platinum–copper nanosheets with thicknesses again tens of thousands of times thinner than a human hair. In fuel-cell evaluations, this catalyst exhibited a 13-fold improvement in mass activity per unit platinum compared with commercial catalysts, and delivered approximately 2.3 times higher performance in full fuel-cell tests. Even after 50,000 accelerated durability cycles, the catalyst retained about 65％ of its initial performance, significantly outperforming conventional catalysts. Importantly, the same performance was achieved while reducing platinum usage by approximately 60％. Professor EunAe Cho emphasized, “This study presents a new catalyst architecture that simultaneously enhances hydrogen production and fuel-cell performance while using far less expensive precious metals,” adding, “It represents a critical turning point for lowering the cost of hydrogen energy and accelerating its commercialization.” <Schematic illustration of ultrathin nanosheet synthesis and transmission electron microscopy (TEM) images of the fabricated catalyst> <Fabrication process of an ultrathin nanosheet catalyst and transmission electron microscopy (TEM) images of the fabricated catalyst> The results of this work were published in two separate papers, both based on the shared core technology of ultrathin nanosheet architectures—one focused on hydrogen-production catalysts and the other on fuel-cell catalysts. The iridium nanosheet study, with doctoral candidate Dongwon Shin as first author, was published online on December 10, 2025, in ACS Nano (IF 16.0). ※ Paper title: “Ultrathin Iridium Nanosheets on Titanium Oxide for High-Efficiency and Durable Proton Exchange Membrane Water Electrolysis,” DOI: 10.1021/acsnano.5c15659 The platinum–copper nanosheet study, with SangJae Lee and doctoral candidate HyunWoo Yang as co–first authors, was published online on December 11, 2025, in Nano Letters (IF 9.6). ※ Paper title: “Ultrathin PtCu Nanosheets: A New Frontier in Highly Efficient and Durable Catalysts for the Oxygen Reduction Reaction,” DOI: 10.1021/acs.nanolett.5c04848 This research was supported by the Energy Human Resource Development Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) under the Ministry of Trade, Industry and Energy, and by the Nano- and Materials-Technology Development Program of the National Research Foundation of Korea under the Ministry of Science and ICT.