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How Small Can Semiconductors Get? KAIST Develops Atomic-Level Prediction Technology
<(From Left) Dr. Tae Hyung Kim, Dr. Juho Lee, (Upper Left) Professor Yong-Hoon Kim>
As the global semiconductor industry enters the so-called "2 nm (nanometer, one-billionth of a meter) process" era, the actual size of transistors — the core components of semiconductor chips — still remains above 10 nm. How much smaller, then, can transistors actually get? KAIST researchers have developed a technology to predict that limit through quantum mechanical atom-level calculations.
KAIST (President Kwang Hyung Lee) announced on the 14th that a research team led by Professor Yong-Hoon Kim of the School of Electrical Engineering has developed a computational design technology that utilizes computer simulations to analyze and predict the scaling limits of transistors, a key challenge in developing next-generation semiconductor devices.
<Research Image(AI-generated)>
Transistors are ultra-small switches that turn electrical currents on and off, serving as key components that determine the performance and power efficiency of semiconductor chips that power smartphones, artificial intelligence computers, and more. The semiconductor industry has continuously downsized transistors to achieve higher performance and lower power consumption. However, when the size becomes excessively small, quantum tunneling occurs—a quantum mechanical phenomenon where electrons pass through energy barriers they normally cannot cross—making current control difficult. For this reason, identifying how much smaller transistors can be made within the boundaries of quantum tunneling is a critical task in next-generation semiconductor development.
However, it is virtually impossible to experimentally confirm the scaling limits of transistors directly. With current technology, it is difficult to precisely control and quantitatively analyze the contact area where the metal electrode and the semiconductor channel (the pathway through which current flows inside a transistor) meet at the atomic level.
The research team resolved this issue by utilizing ab initio or first-principles calculations, a method that computes material properties based solely on fundamental physics laws without relying on experimental data. The research team had previously developed and reported a new theoretical-computational framework called multi-space constrained-search density functional theory (MS-DFT), which extends the scope of first-principles calculations from materials to devices by precisely analyzing the complex quantum phenomena occurring at the interface where metal electrodes and semiconductors meet and across which electrons flow.
In this study, the team built on this framework to perform computational transfer length method (TLM) experiments, the gold standard experimental technique for extracting contact resistance (the resistance to current flow occurring at the metal electrode-semiconductor interface). Based on the atomic-level TLM calculations results, they identified the quantum tunneling limit (the length at which electrons stop leaking and begin to allow transistor current control).
The research team applied this technology to a monolayer MoS₂ (molybdenum disulfide) device, a representative two-dimensional semiconductor material that can be made as thin as an atomic layer and is a candidate material for next-generation transistor channels. As a result, they were able to quantitatively analyze how deeply electrons penetrate into the channel and how much this hinders current flow control depending on the type of metal electrode and the contact atomic geometry. In other words, they clarified that the limit to how small a transistor can be made varies depending on which metal and contact structure are selected. This implies that the performance and limits of a device can now be predicted in advance solely through computer simulations before the actual transistor fabrication.
< Analysis of Contact Resistance and Critical Tunneling Length in Two-Dimensional Semiconductors Using the First-Principles Transfer Length Method >
According to the research results, the critical tunneling length—the maximum length at which electrons penetrate into the channel and begin to affect transistor operation—was found not to be a single fixed value. This length emerged as a design variable that changes depending on the work function of the metal (the minimum energy required to remove an electron from a metal) and the contact structure of the interface where the metal and semiconductor meet. This signifies that the extent to which a transistor can be downsized depends on the combination of materials and structural design.
In particular, among the candidate metal types and contact structures considered, the research team confirmed that the length where electrons stop leaking could be reduced to less than 4 nm. This result demonstrates the possibility of making transistors even smaller than the levels achieved today.
Furthermore, the research team proposed a design strategy for next-generation semiconductor chips that reduce power consumption by combining two-dimensional semiconductors with different properties.
This study is significant because it establishes a platform for predicting scaling limits and designing optimal device configurations before actually fabricating semiconductor chips. Through this, it is expected to reduce trial and error and shorten the development period in the process of developing next-generation ultra-small AI semiconductor devices.
Professor Yong-Hoon Kim said, "This study is significant because it presents a new physical criterion for defining how small next-generation transistors can become. By computationally analyzing quantum mechanical phenomena in the sub-10 nm regime, which are difficult to probe experimentally, we have opened a path toward utilizing these findings in next-generation transistor design."
The study, in which Dr. Tae Hyung Kim participated as the first author, was published online on May 28th in the prestigious computational journal 'npj Computational Materials, a prestigious journal in the field of computational materials science ※ Title of the paper: Ab initio transfer length method simulations of tunneling limits in 2D semiconductors, DOI: https://doi.org/10.1038/s41524-026-02101-1
This research was conducted with support from programs such as the Mid-Career Researcher Program and EDISON 2.0 Program of the National Research Foundation of Korea.
2026.06.15 View 167 -
Professor Hoon Sohn of the Department of Civil and Environmental Engineering Selected as the June Winner of the 'Korea Scientist and Engineer Award'
< Professor Hoon Sohn, Department of Civil and Environmental Engineering >
Professor Hoon Sohn from KAIST Department of Civil and Environmental Engineering has been selected as the June winner of the 'Korea Scientist and Engineer Award.'
The Korea Scientist and Engineer Award is presented monthly by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) to a researcher who has made significant contributions to the advancement of science and technology through original research achievements over the past three years. The award includes a commendation from the Deputy Prime Minister and Minister of Science and ICT, along with a cash prize of 10 million KRW.
Professor Hoon Sohn was recognized for his contributions to developing an affordable, high-precision displacement sensor technology capable of detecting disaster and hazard risks in small-to-medium-sized infrastructure in real-time.
With the rapid aging of infrastructure such as bridges and buildings in recent years, the importance of technology that continuously monitors the structural safety of facilities has been growing. However, small-to-medium-sized structures—which make up the vast majority of infrastructure worldwide—exhibit very subtle movements on a millimeter scale, requiring highly precise measurement. Moreover, existing equipment is prohibitively expensive, making widespread adoption difficult.
To overcome these limitations, Professor Sohn combined millimeter-wave (mmWave) radar with Micro-Electro-Mechanical Systems (MEMS) accelerometers and applied signal processing algorithms. Through this, he successfully developed a technology that can simultaneously measure a structure's vibration, tilt, and displacement with a single sensor.
The production cost of this sensor is under 1 million KRW, which is approximately 1/40th the cost of conventional equipment, yet it boasts a high precision of 0.026 mm. Its power consumption has also been reduced to 1/100th of existing systems. Furthermore, it incorporates energy harvesting technology that utilizes ambient wasted energy, allowing for completely wireless operation.
The reliability of this technology has been proven through field demonstrations at more than 13 domestic and international sites, including a parking garage at Stanford University (USA), a highway in San Jose (USA), a bridge in Weifang (China), and the Geumgang Pedestrian Bridge in Sejong (South Korea).
Professor Sohn stated, "The significance of this research lies in establishing a technological foundation to precisely manage small-to-medium-sized structures that have previously been excluded from continuous, routine monitoring." He added, "Moving forward, I will continue my research on AI-based digital twins to lead the automation, unmanned operation, and intelligent advancement of the safety diagnosis market, thereby contributing to public safety and disaster prevention."
2026.06.04 View 267 -
KAIST Produces Eco-Friendly Core Nylon Precursors Used from Clothing to Automobiles with Microbes
<(From Left) Dr. Da-Hee Ahn, Distinguished Professor Sang Yup Lee>
Nylon is a representative plastic material used throughout our daily lives, from clothing to automobiles. However, most of its raw materials have been produced through petrochemical processes, resulting in large carbon emissions. KAIST researchers have developed a technology that can produce key nylon precursors in an eco-friendly way using microbes.
KAIST (President Kwang Hyung Lee) announced on the 31st of May that a research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering has developed an Escherichia coli-based modular platform capable of producing three key monomers (basic molecular units that make up polymers) of “nylon 6,6” and “nylon 6” — adipic acid, hexamethylenediamine, and epsilon-caprolactam — from “glycerol (an eco-friendly bio-based byproduct generated during biodiesel production),” a renewable carbon source, using systems metabolic engineering (a technology that designs and optimizes microbial metabolic pathways to maximize the production of desired substances).
“Nylon 6” is highly flexible and is used in clothing and films, while “nylon 6,6” has excellent strength and heat resistance and is used in automobiles and machinery parts. The numbers after the nylon name indicate the number of carbon atoms contained in the raw material molecules.
The core of this study is that the biosynthetic pathway was divided into upstream and downstream modules, with E. coli strains assigned different roles. The upstream strain was designed to produce adipic acid from glycerol, while the downstream strain was designed to convert it into hexamethylenediamine or epsilon-caprolactam, respectively. Through this, the research team succeeded in producing adipic acid and hexamethylenediamine, the key raw materials of nylon 6,6, and epsilon-caprolactam, the key raw material of nylon 6, within a single integrated platform.
To improve production efficiency, the researchers compared and validated various enzymes (proteins that promote chemical reactions in living organisms), including carboxylic acid reductases and transaminases, and applied the optimal combination, thereby improving hexamethylenediamine titer. In addition, in the epsilon-caprolactam production process, they designed a flexible-linker fusion enzyme that enhances reaction efficiency through efficient cofactor regeneration.In the upstream module, the team reconstructed the biosynthetic pathway (a series of reaction processes through which compounds are produced in living organisms) and improved the performance of key enzymes using artificial intelligence (AI), increasing production titer. As a result, they succeeded in producing adipic acid at a level of 6 grams per liter (g/L) in a fed-batch fermentation process.
The research team also applied a “delayed inoculation” strategy (time-staggered co-culture), in which the second strain is introduced later after sufficient adipic acid has first been produced, rather than adding the two types of E. coli simultaneously. This is a method of sequentially introducing microbes with different roles at different times.
When this strategy was applied to a fed-batch fermentation process (a fermentation method that increases productivity by supplying nutrients step by step), the team produced 230 milligrams per liter (mg/L) of hexamethylenediamine and 808 micrograms per liter (μg/L) of epsilon-caprolactam using only glycerol. Although the production amounts are not yet high, the research team explained that these results represent world-class performance among cases of direct production from glycerol.
<Schematic Diagram>
This technology is significant in that it presents the possibility of producing nylon raw materials, which have relied on petrochemical processes, through bio-based methods.
The research team plans to further improve titer by combining AI-based enzyme design with additional systems metabolic engineering, and to expand the platform to produce various polymer raw materials (substances formed by the repeated bonding of multiple monomers).
Distinguished Professor Sang Yup Lee stated, “This study is meaningful in that it presents a modular microbial platform capable of producing key monomers required for nylon 6 and nylon 6,6 production from renewable carbon sources,” adding, “We will continue to advance enzyme and metabolic flux engineering to improve titer and develop this into a core platform for sustainably producing various bio-based polymer raw materials.”
The results of this study were published on May 4 in the Proceedings of the National Academy of Sciences (PNAS), with Dr. Da-Hee Ahn of the Department of Chemical and Biomolecular Engineering as the first author.
※ Paper title: “Metabolic engineering of Escherichia coli for the biosynthesis of nylon 6 and nylon 6,6 monomers”
Authors: Sang Yup Lee (KAIST, corresponding author), Da-Hee Ahn (KAIST, first author), Tong Un Chae (KAIST, second author), total of 3 authors
DOI: https://doi.org/10.1073/pnas.2535786123
This research was supported by the “Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries” project under the Petroleum Replacement Eco-Friendly Chemical Technology Development Program supported by the Ministry of Science and ICT, and by the “Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry” project under the Core Synthetic Biology Technology Development Program.
2026.06.01 View 431 -
KAIST Develops Self-Regenerating Catalyst That Restores Its Own Performance, Opening a Breakthrough for CO₂ Conversion Technology
<(From Left) Professor Dong Young Chung, Ph.D Candidate Hongmin An, Hanjoo Kim>
Technologies that convert carbon dioxide (CO₂) emitted from factories and power plants into useful chemical feedstocks are considered key to achieving carbon neutrality. However, rapid degradation of catalyst performance has long hindered commercialization. KAIST researchers have now developed a “self-regenerating” catalyst that restores its activity during operation, offering a potential solution to this challenge.
KAIST (President Kwang Hyung Lee) announced on the 11th of March that a research team led by Professor Dong Young Chung from the Department of Chemical and Biomolecular Engineering has identified the fundamental cause of catalyst degradation in electrochemical reactions that convert CO₂ into useful materials and has developed a new design strategy that allows catalysts to maintain their active state during the reaction.
<Schematic Illustration of Copper Catalyst Reconstruction>
The research team focused particularly on copper (Cu) catalysts, which are widely used in CO₂ conversion reactions. Copper catalysts are known not to simply degrade during reactions but instead undergo a process called surface reconstruction, in which their surface structure continuously changes. The study revealed that the performance and lifetime of the catalyst vary significantly depending on how this reconstruction occurs.
The researchers discovered that copper catalyst reconstruction occurs mainly through two different mechanisms. The first involves formation and reduction of oxide layers on the catalyst surface. While this temporarily increases catalytic activity, it ultimately leads to long-term degradation of catalyst performance.
The second mechanism involves partial dissolution of the catalyst metal into the electrolyte followed by redeposition onto the catalyst surface. During this process, new reactive sites—known as active sites—are continuously created on the catalyst surface.
Based on this mechanism, the team proposed a method that allows the catalyst to maintain its active state during the reaction. By introducing a trace amount of copper ions into the electrolyte, dissolution and redeposition of copper occur in a balanced cycle on the catalyst surface. This continuous cycle generates new active sites, enabling the catalyst to maintain stable performance over extended periods.
Importantly, this technology can be implemented without complex additional processes or high-voltage conditions, significantly reducing energy consumption while enabling stable production of high-value C₂ compounds such as ethylene and ethanol. C₂ compounds are molecules containing two carbon atoms and are industrially important chemicals used as feedstocks for plastics, fuels, and other materials.
This research is significant because it proposes a new design concept in which catalysts are not merely optimized at the initial stage but are engineered to maintain their optimal state throughout the reaction process. The concept is expected to be applicable not only to CO₂ conversion technologies but also to a wide range of electrochemical energy conversion systems.
Professor Dong Young Chung stated, “This research approached catalyst degradation not as an inevitable phenomenon but as a controllable process,” adding, “We proposed a new strategy that allows catalysts to continuously maintain optimal activity during the reaction.”
The study was led by Hanjoo Kim, a doctoral student at KAIST, and Hongmin An, a combined master’s-doctoral student, as co-first authors. The research was published online on February 5 in the Journal of the American Chemical Society (JACS), one of the world’s most prestigious journals in chemistry.
※ Paper title: “Dynamic Interface Engineering via Mechanistic Understanding of Copper Reconstruction in Electrochemical CO₂ Reduction Reaction” DOI: 10.1021/jacs.5c16244
This research was supported by the Global Young Connect Program for Materials and the National Strategic Materials Technology Development Program funded through the National Research Foundation of Korea.
2026.03.11 View 1613 -
KAIST Develops mRNA Platform That Remains Effective Even in Aging and Obesity
<(From Left) Dr. Subin Yoon, Ph.D candidate Hyeonggon Cho, Prof. Jae-Hwan Nam, Prof. Young-suk Lee>
Since the COVID-19 pandemic, mRNA vaccines have gained attention as a next-generation pharmaceutical technology. mRNA therapeutics work by delivering genetic instructions that enable cells to produce specific proteins for therapeutic effects. However, their efficacy has been reported to decline in elderly individuals or patients with obesity. To address this limitation, Korean researchers have newly designed a key regulatory region of mRNA that improves therapeutic protein production efficiency, developing a next-generation mRNA platform that maintains effectiveness even in aging and obesity conditions.
KAIST (President Kwang Hyung Lee) announced on the 10th of March that a joint research team led by Professor Young-suk Lee of the Department of Bio and Brain Engineering and Professor Jae-Hwan Nam of The Catholic University of Korea (President Jun-Gyu Choi) has developed a new mRNA platform by precisely designing the sequence of the 5′ untranslated region (5′UTR)*, a key regulatory region of mRNA.*5′ untranslated region (5′UTR): A region of mRNA that initiates and regulates protein production. The design of this region influences both the amount and speed of protein synthesis.
The research team analyzed large-scale bioinformatics datasets to identify 5′UTR sequences that enable proteins to be produced more efficiently across diverse cellular environments. When applied, the designed sequences significantly enhanced protein production and immune responses even in preclinical models of aging and obesity.
mRNA is a long single-stranded RNA molecule that serves as the blueprint for producing proteins required by the body. It consists of several components: the 5′UTR, which initiates and regulates the rate of protein production; the coding sequence (CDS), which contains the genetic information for a specific protein; the 3′ untranslated region (3′UTR), which helps maintain mRNA stability within cells; and the poly(A) tail, which further enhances stability and supports protein synthesis.
Among these components, the 5′UTR and 3′UTR do not determine the type of protein produced, but they play a critical role in regulating how efficiently the protein is synthesized. For this reason, these regions are receiving increasing attention as key bioengineering platforms for improving the performance of various mRNA therapeutics, including vaccines and treatments.
<Schematic Diagram of mRNA Therapeutic Design and Validation Using Bioinformatics>
To identify highly efficient 5′UTR sequences capable of promoting protein production across multiple tissues and cellular environments, the team conducted an integrated analysis of large-scale biological datasets. This included multiple analytical approaches such as RNA sequencing (RNA-seq) for analyzing gene activity across tissues, single-cell RNA sequencing (scRNA-seq) for examining gene expression at the individual cell level, and ribosome profiling (Ribo-seq) for measuring actual protein translation efficiency.
The researchers also focused on the fact that in aging or obesity conditions, cells often experience high levels of stress—particularly oxidative stress—which can reduce their ability to synthesize proteins. When the newly designed mRNA therapeutics were applied to preclinical models of aging and obesity, the results showed significantly improved protein production and immune responses compared with existing approaches. This research is expected to be applicable not only to mRNA vaccines but also to a wide range of biopharmaceutical technologies, including gene therapies and immunotherapies.
<Multimodal Bio–Big Data Analysis–Based mRNA Therapeutic Design (AI-Generated Image)>
Professor Young-suk Lee of KAIST Department of Bio and Brain Engineering stated, “This study identified a design strategy that enables mRNA to produce proteins more efficiently by analyzing large-scale biological data,” adding, “This technology will provide an important foundation for ensuring that mRNA vaccines and therapeutics remain effective even in environments where drug efficacy may decline, such as in elderly or obese patients.”
In this study, Dr. Subin Yoon from The Catholic University of Korea and doctoral candidate Hyeonggon Cho from KAIST participated as co-first authors. The research findings were published online on January 2 in the internationally renowned journal Molecular Therapy (IF = 12.0), a leading journal in gene and cell therapy.
(Paper title: ”Designing 5′UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity” DOI: https://doi.org/10.1016/j.ymthe.2025.12.060)
This research was supported by the Excellent Young Researcher Program and the Bio-Medical Technology Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, the Infectious Disease Response Innovative Technology Support Program of the Ministry of Food and Drug Safety, and the Infectious Disease Prevention and Therapeutics Technology Development Program of the Korea Health Industry Development Institute.
2026.03.10 View 1656 -
Professor Kuk-Jin Yoon’s Research Team at the Department of Mechanical Engineering Achieves Landmark Success with 10 Papers Accepted at CVPR 2026
<Professor Kuk-Jin Joon from Department of Mechanical Engineering>
Professor Kuk-Jin Yoon’s research team from our university’s Department of Mechanical Engineering has once again demonstrated its overwhelming academic prowess by having a total of 10 papers accepted as lead authors at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026).
CVPR is the most influential international conference in the fields of artificial intelligence and visual intelligence. Since its inception in 1983, it has selected outstanding research through a rigorous peer-review process every year. For CVPR 2026, a total of 16,092 papers were submitted worldwide, with 4,090 accepted, resulting in a competitive acceptance rate of approximately 25.42%. Achieving 10 accepted papers as lead or corresponding authors from a single laboratory is regarded as an exceptionally rare and world-class feat.
Professor Kuk-Jin Yoon’s team conducts extensive research with the ultimate goal of achieving human-level visual intelligence. The papers accepted this year cover cutting-edge topics in computer vision, including:
Event camera-based technologies
Perception technologies for autonomous driving
AI optimization and adaptation techniques
This achievement follows the team's remarkable success at ICCV 2025 last year, where they published 12 papers as lead/corresponding authors. The results at CVPR 2026 further solidify the laboratory's position as a global hub for pioneering computer vision research. The research team plans to continue contributing to the advancement of future AI technologies by tackling challenging research that transcends the limitations of existing methods.
Meanwhile, CVPR 2026 is scheduled to be held in Denver, Colorado, USA, from June 3 to June 7.
<CVPR 2026 (Denver, USA)>
2026.03.06 View 3292 -
Designing the Heart of Hydrogen Cars with AI... Development of Next-Generation Super Catalyst
<(From left) KAIST Ph.D. Candidate HyunWoo Chang, Professor EunAe Cho. (Top, from left) Seoul National University Professor Won Bo Lee, Dr. Jae Hyun Ryu.>
In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the ‘heart of the hydrogen car,’ still faces limitations of high cost and short lifespan. The core cause is the platinum catalyst. While it is a decisive material for generating electricity, the reaction is slow, performance degrades over time, and manufacturing costs are high. Korean researchers have presented a clue to solving this difficult problem.
KAIST announced on February 26th that the research team led by Professor EunAe Cho of the Department of Materials Science and Engineering, together with the team of Professor Won Bo Lee of the School of Chemical and Biological Engineering at Seoul National University, has developed a technology that predicts the ‘atomic arrangement’ tendency of catalysts using artificial intelligence (AI).
This technology is akin to calculating beforehand which combination is advantageous for completing a puzzle before putting it together. By having AI calculate the arrangement speed of metal atoms first, it has become possible to efficiently design catalysts with better performance. The core of this research is that ‘AI revealed the fact that zinc plays a decisive role in the platinum-cobalt atomic arrangement.’
<Schematic diagram of AI-based atomic alignment prediction>
Despite the high performance of existing platinum-cobalt (Pt-Co) alloy catalysts, very high-temperature heat treatment was required to create the ‘intermetallic (L1₀)’ structure, where atoms are regularly arranged. In this process, particles would clump together, or the structure would become unstable, posing limitations for actual fuel cell application.
To solve this problem, the research team introduced machine learning-based quantum chemistry simulations. Through AI, they precisely predicted how atoms move and arrange themselves inside the catalyst.
As a result, they discovered that zinc (Zn) acts as a mediating element that promotes atomic arrangement. The principle is that when zinc is introduced, atoms find their places more easily, forming a more sophisticated and stable structure. In other words, AI has found the ‘optimal path for atomic arrangement creation’ in advance.
< Synthesis process of Zinc-introduced Platinum-Cobalt catalyst>
The zinc-platinum-cobalt catalyst, synthesized based on AI predictions, secured both higher activity and superior long-term durability compared to commercial platinum catalysts. This is a case proving that the ‘virtual blueprint’ calculated by artificial intelligence can be implemented as a high-performance catalyst in an actual laboratory.
In particular, this technology is expected to contribute to extending catalyst lifespan and reducing manufacturing costs across core carbon-neutral industries, such as hydrogen passenger cars, hydrogen trucks requiring long-distance operation, hydrogen ships, and energy storage systems (ESS).
< Conceptual diagram of AI-based catalyst development (AI-generated image) >
Professor EunAe Cho stated, “This research is a case of utilizing machine learning to predict the atomic arrangement tendency of catalysts in advance and implementing this through actual synthesis,” and added, “AI-based material design will become a new paradigm for the development of next-generation fuel cell catalysts.”
Ph.D. Candidate HyunWoo Chang from KAIST’s Department of Materials Science and Engineering and Dr. Jae Hyun Ryu from Seoul National University’s School of Chemical and Biological Engineering participated as co-first authors in this research. The research results were published on January 15, 2026, in ‘Advanced Energy Materials,’ a world-renowned academic journal in the energy materials field. ※ Paper Title: Machine Learning-Guided Design of L1₀-PtCo Intermetallic Catalysts: Zn-Mediated Atomic Ordering, DOI: https://doi.org/10.1002/aenm.202505211
This research was conducted with the support of the National Research Foundation of Korea’s Nano & Material Technology Development Program and the Korea Institute of Energy Technology Evaluation and Planning’s Energy Innovation Research Center for Fuel Cell Technology.
2026.02.27 View 1941 -
KAIST Launches Deep-Tech Scale-up Valley, Unveils Execution Strategies for Physical AI
< Progress Report Meeting of the Deep-Tech Scale-up Valley Project >
KAIST announced on February 27th that it held the "Deep-Tech Scale-up Valley Project Progress Report Meeting" at its main campus in Daejeon on the 26th. During the meeting, the university unveiled its Physical AI strategies and execution structures, currently being developed with a focus on robotics.
The Deep-Tech Scale-up Valley Promotion Project is a joint initiative by the Ministry of Science and ICT, Daejeon Metropolitan City, and KAIST. KAIST has secured a total budget of 13.65 billion KRW for a period of three years and six months, starting from 2025. The project aims to commercialize KAIST's deep-tech capabilities in robotics to build a robust robot innovation ecosystem. A "Robot Alliance" has been formed, led by KAIST (headed by Professor Jung Kim) and including KAIST Holdings, Daejeon Techno Park, Daejeon Center for Creative Economy & Innovation, Angel Robotics, and Eurobotics.
The project seeks to foster a virtuous cycle ecosystem and nurture future "Unicorn" companies based on a three-pillar framework: Technology Commercialization, Deep-Tech R&D, and Commercialization Scale-up. In its first year (2025), the project achieved 230 billion KRW in technology transfers and investment attraction through Physical AI lectures, startup pitching sessions, and investment networking.
Physical AI refers to technology that combines robotics with artificial intelligence, allowing machines to make autonomous decisions and act in the real world. While it is gaining traction as a core field of next-generation industry—with increasing government R&D, corporate investment, and startup activity—critics have noted that successful business models applicable to actual industrial sites remain limited.
This report meeting is significant in that it redefined Physical AI not merely as a competition of AI technology, but as a matter of "industrial structure." It emphasized that commercialization is difficult unless R&D, industrial sites, and the investment ecosystem are organically linked.
Specifically, the report stated that for Physical AI to be applied to industrial sites, "meaningful data" generated from real-world operations is required, going beyond virtual environments. The strategy involves collaborating with skilled experts in manufacturing processes to accumulate data reflecting physical sensations and judgment, and establishing an execution system where robots can continuously cooperate with humans without obstructing their tasks.
Professor Kyoungchul Kong of the KAIST Department of Mechanical Engineering stated, "It is now crucial to clarify the mixed concepts of Physical AI and create a concrete platform that anyone can utilize." He added, "For AI learned in virtual environments to function properly with actual robots in the real world, we must not only improve the accuracy of virtual technologies but also ensure that physical variables in the real world are predictable and stably managed." In simpler terms, technology is needed to ensure that a robot's performance in a simulation translates seamlessly to the real world.
Professor Hyun Myung of the KAIST School of Electrical Engineering highlighted, "In the field of AI, research on Physics-Informed Neural Networks (PINN), which incorporate physical laws into the learning process, is actively underway." He emphasized, "The completion of Physical AI is possible only when hardware researchers, who understand actual physical systems, and AI researchers, who implement these into learning structures, are organically integrated. We need AI that understands physical principles, going beyond simply learning massive amounts of data."
Based on this execution structure, KAIST plans to establish a clear Value Chain connecting researchers, industrial experts, and corporations. The strategy is to expand Physical AI from lab-scale demonstrations to technologies that solve real-world industrial problems.
Jung Kim, Head of the KAIST Department of Mechanical Engineering, stated, "We have moved past the era of competing on data volume; now is the time to contemplate how to execute AI in the physical world. Based on KAIST's specific preparations and execution strategies, we will support startups and companies to succeed in the commercialization of Physical AI."
Meanwhile, the Deep-Tech Scale-up Valley Project plans to step-by-step promote the establishment of a Physical AI platform, startup discovery and investment expansion, the creation of verification testbeds, and the expansion of cooperation networks with global robotics companies.
2026.02.27 View 2432 -
KAIST Overcomes Limitations of Existing Image Sensors… Clear Colors Even Under Oblique Light
<(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.”
2026.02.19 View 1902 -
KAIST detects ‘hidden defects’ that degrade semiconductor performance with 1,000× higher sensitivity
<(From Left) Professor Byungha Shin, Ph.D candidate Chaeyoun Kim, Dr. Oki Gunawan>
Semiconductors are used in devices such as memory chips and solar cells, and within them may exist invisible defects that interfere with electrical flow. A joint research team has developed a new analysis method that can detect these “hidden defects” (electronic traps) with approximately 1,000 times higher sensitivity than existing techniques. The technology is expected to improve semiconductor performance and lifetime, while significantly reducing development time and costs by enabling precise identification of defect sources.
KAIST (President Kwang Hyung Lee) announced on January 8th that a joint research team led by Professor Byungha Shin of the Department of Materials Science and Engineering at KAIST and Dr. Oki Gunawan of the IBM T. J. Watson Research Center has developed a new measurement technique that can simultaneously analyze defects that hinder electrical transport (electronic traps) and charge carrier transport properties inside semiconductors.
Within semiconductors, electronic traps can exist that capture electrons and hinder their movement. When electrons are trapped, electrical current cannot flow smoothly, leading to leakage currents and degraded device performance. Therefore, accurately evaluating semiconductor performance requires determining how many electronic traps are present and how strongly they capture electrons.
The research team focused on Hall measurements, a technique that has long been used in semiconductor analysis. Hall measurements analyze electron motion using electric and magnetic fields. By adding controlled light illumination and temperature variation to this method, the team succeeded in extracting information that was difficult to obtain using conventional approaches.
Under weak illumination, newly generated electrons are first captured by electronic traps. As the light intensity is gradually increased, the traps become filled, and subsequently generated electrons begin to move freely. By analyzing this transition process, the researchers were able to precisely calculate the density and characteristics of electronic traps.
The greatest advantage of this method is that multiple types of information can be obtained simultaneously from a single measurement. It allows not only the evaluation of how fast electrons move, how long they survive, and how far they travel, but also the properties of traps that interfere with electron transport.
The team first validated the accuracy of the technique using silicon semiconductors and then applied it to perovskites, which are attracting attention as next-generation solar cell materials. As a result, they successfully detected extremely small quantities of electronic traps that were difficult to identify using existing methods—demonstrating a sensitivity approximately 1,000 times higher than that of conventional techniques.
< Conceptual Diagram of the Evolution of Hall Characterization (Analysis) Techniques >
Professor Byungha Shin stated, “This study presents a new method that enables simultaneous analysis of electrical transport and the factors that hinder it within semiconductors using a single measurement,” adding that “it will serve as an important tool for improving the performance and reliability of various semiconductor devices, including memory semiconductors and solar cells.”
The results of this research were published on January 1 in Science Advances, an international academic journal, with Chaeyoun Kim, a doctoral student in the Department of Materials Science and Engineering, as the first author.
※ Paper title: “Electronic trap detection with carrier-resolved photo-Hall effect,” DOI: https://doi.org/10.1126/sciadv.adz0460
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
< Conceptual Diagram of Charge Transport and Trap Characterization Using Photo-Hall Measurements (AI-generated image) >
2026.01.08 View 2329 -
Breaking Performance Barriers of All Solid State Batteries
< (Bottom, from left) Professor Dong-Hwa Seo, Researcher Jae-Seung Kim, (Top, from left) Professor Kyung-Wan Nam, Professor Sung-Kyun Jung, Professor Youn-Seok Jung >
Batteries are an essential technology in modern society, powering smartphones and electric vehicles, yet they face limitations such as fire explosion risks and high costs. While all-solid-state batteries have garnered attention as a viable alternative, it has been difficult to simultaneously satisfy safety, performance, and cost. Recently, a Korean research team successfully improved the performance of all-solid-state batteries simply through structural design—without adding expensive metals.
KAIST announced on January 7th that a research team led by Professor Dong-Hwa Seo from the Department of Materials Science and Engineering, in collaboration with teams led by Professor Sung-Kyun Jung (Seoul National University), Professor Youn-Suk Jung (Yonsei University), and Professor Kyung-Wan Nam (Dongguk University), has developed a design method for core materials for all-solid-state batteries that uses low-cost raw materials while ensuring high performance and low risk of fire or explosion.
Conventional batteries rely on lithium ions moving through a liquid electrolyte. In contrast, all-solid-state batteries use a solid electrolyte. While this makes them safer, achieving rapid lithium-ion movement within a solid has typically required expensive metals or complex manufacturing processes.
To create efficient pathways for lithium-ion transport within the solid electrolyte, the research team focused on "divalent anions" such as oxygen and sulfur . Divalent anions play a crucial role in altering the crystal structure by integrating into the basic framework of the electrolyte.
The team developed a technology to precisely control the internal structure of low-cost zirconium (Zr)-based halide solid electrolytes by introducing these divalent anions. This design principle, termed the "Framework Regulation Mechanism," widens the pathways for lithium ions and lowers the energy barriers they encounter during transport. By adjusting the bonding environment and crystal structure around the lithium ions, the team enabled faster and easier movement.
To verify these structural changes, the researchers utilized various high-precision analysis techniques, including:
High-energy Synchrontron X-ray diffraction(Synchrotron XRD)
Pair Distribution Function (PDF) analysis
X-ray Absorption Spectroscopy (XAS)
Density Functional Theory (DFT) modeling for electronic structure and diffusion.
The results showed that electrolytes incorporating oxygen or sulfur improved lithium-ion mobility by 2 to 4 times compared to conventional zirconium-based electrolytes. This signifies that performance levels suitable for practical all-solid-state battery applications can be achieved using inexpensive materials.
Specifically, the ionic conductivity at room temperature was measured at approximately 1.78 mS/cm for the oxygen-doped electrolyte and 1.01 mS/cm for the sulfur-doped electrolyte. Ionic conductivity indicates how quickly and smoothly lithium ions move; a value above 1 mS/cm is generally considered sufficient for practical battery applications at room temperature.
< Structural Regulation Mechanism of Zr-based Halide Electrolytes via Divalent Anion Introduction >
< Atomic Rearrangement of Solid Electrolyte for All-Solid-State Batteries (AI-generated image) >
Professor Dong-Hwa Seo stated, "Through this research, we have presented a design principle that can simultaneously improve the cost and performance of all-solid-state batteries using cheap raw materials. Its potential for industrial application is very high." Lead author Jae-Seung Kim added that the study shifts the focus from "what materials to use" to "how to design them" in the development of battery materials.
This study, with Jae-Seung Kim (KAIST) and Da-Seul Han (Dongguk University) as co-first authors, was published in the international journal Nature Communications on November 27, 2025.
Paper Title: Divalent anion-driven framework regulation in Zr-based halide solid electrolytes for all-solid-state batteries
DOI: https://www.nature.com/articles/s41467-025-65702-2
This research was supported by the Samsung Electronics Future Technology Promotion Center, the National Research Foundation of Korea, and the National Supercomputing Center.
2026.01.07 View 5299 -
Direct Printing of Nanolasers, the Key to Optical Computing and Quantum Security
< (From left) Professor Ji Tae Kim (KAIST), Dr. Shiqi Hu (First Author, AI-based Intelligent Design-Manufacturing Integrated Research Group, KAIST-POSTECH), and Professor Junsuk Rho (POSTECH) >
In future high-tech industries, such as high-speed optical computing for massive AI, quantum cryptographic communication, and ultra-high-resolution augmented reality (AR) displays, nanolasers—which process information using light—are gaining significant attention as core components for next-generation semiconductors. A research team at our university has proposed a new manufacturing technology capable of high-density placement of nanolasers on semiconductor chips, which process information in spaces thinner than a human hair.
KAIST announced on January 6th that a joint research team, led by Professor Ji Tae Kim from the Department of Mechanical Engineering and Professor Junsuk Rho from POSTECH (President Seong-keun Kim), has developed an ultra-fine 3D printing technology capable of creating "vertical nanolasers," a key component for ultra-high-density optical integrated circuits.
Conventional semiconductor manufacturing methods, such as lithography, are effective for mass-producing identical structures but face limitations: the processes are complex and costly, making it difficult to freely change the shape or position of devices. Furthermore, most existing lasers are built as horizontal structures lying flat on a substrate, which consumes significant space and suffers from reduced efficiency due to light leakage into the substrate.
To solve these issues, the research team developed a new 3D printing method to vertically stack perovskite, a next-generation semiconductor material that generates light efficiently. This technology, known as "ultra-fine electrohydrodynamic 3D printing," uses electrical voltage to precisely control invisible ink droplets at the attoliter scale ($10^{-18}$ L).
Through this method, the team successfully printed pillar-shaped nanostructures—much thinner than a human hair—directly and vertically at desired locations without the need for complex subtractive processes (carving material away).
The core of this technology lies in significantly increasing laser efficiency by making the surface of the printed perovskite nanostructures extremely smooth. By combining the printing process with gas-phase crystallization control technology, the team achieved high-quality structures with nearly single-crystalline alignment. As a result, they were able to realize high-efficiency vertical nanolasers that operate stably with minimal light loss.
Additionally, the team demonstrated that the color of the emitted laser light could be precisely tuned by adjusting the height of the nanostructures. Utilizing this, they created laser security patterns invisible to the naked eye—identifiable only with specialized equipment—confirming the potential for commercialization in anti-counterfeiting technology.
< 3D Printing of Perovskite Nanolasers >
Professor Jitae Kim stated, "This technology allows for the direct, high-density implementation of optical computing semiconductors on a chip without complex processing. It will accelerate the commercialization of ultra-high-speed optical computing and next-generation security technologies."
The research results, with Dr. Shiqi Hu from the Department of Mechanical Engineering as the first author, were published online on December 6, 2025, in ACS Nano, an international prestigious journal in the field of nanoscience.
Paper Title: Nanoprinting with Crystal Engineering for Perovskite Lasers
DOI: https://doi.org/10.1021/acsnano.5c16906
This research was conducted with support from the Ministry of Science and ICT’s Excellent Young Researcher Program (RS-2025-00556379), the Mid-career Researcher Support Program (RS-2024-00356928), and the InnoCORE AI-based Intelligent Design-Manufacturing Integrated Research Group (N10250154).
2026.01.06 View 2668