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KAIST Presents Roadmap for AFM Utilization in Next-Generation Semiconductor and Energy Materials Research
<(From Left) Ph. D candidate Yeongyu Kim, Professor Seungbum Hong, Ph.D candidate Kunwoo Park>
For smartphones and computers to become smaller and faster, technologies capable of precisely controlling electrical properties at the nanoscale—beyond what is visible to the naked eye—are essential. In particular, ferroelectric materials, which can maintain their electrical state without external power, are gaining attention as key components for next-generation memory and sensor technologies. However, due to their extremely small size, there have been limitations in precisely observing the internal changes occurring within these materials.
KAIST (President Kwang Hyung Lee) announced on the 4th of April that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering has published a review paper systematically outlining research strategies for ferroelectric materials based on atomic force microscopy (AFM), addressing these limitations.
The research team proposed new strategies for utilizing AFM to precisely control electrical properties at the nanoscale and presented a direction for next-generation materials research.
Ferroelectric materials possess electric polarization similar to magnetism, and this property enables the realization of memory devices that retain information even without power, as well as highly sensitive sensors. As semiconductor devices continue to shrink, nanoscale physical phenomena increasingly determine overall device performance, making technologies capable of precisely analyzing and controlling these phenomena more important than ever.
The team presented an integrated analytical framework that uses AFM to both observe and directly manipulate materials at the nanoscale. AFM is a device that scans surfaces using an extremely fine probe to obtain atomic-level information, effectively serving as both the “eye” and “hand” of the nanoscale world.
Based on AFM, which measures physical and electrical properties at the atomic scale by scanning surfaces with a fine probe, the researchers established a system that integrates various techniques—including piezoresponse force microscopy (PFM) for measuring electrical responses, Kelvin probe force microscopy (KPFM) for analyzing surface potential, and conductive atomic force microscopy (C-AFM) for measuring current flow—into a unified framework. This allows for a three-dimensional understanding of material structures and charge distributions.
This approach goes beyond simple observation and represents the evolution of AFM into a research platform capable of directly designing and manipulating data domains at the nanoscale by applying electrical stimuli through the probe.
Furthermore, AFM can apply electrical stimulation or mechanical pressure directly to extremely small nanoscale regions, enabling changes and control of material properties. In other words, it has evolved from a tool that merely observes to one that enables design and experimentation at the nanoscale. In particular, this study demonstrates applications in evaluating and improving the performance of next-generation semiconductor materials such as two-dimensional transition metal dichalcogenides like molybdenum disulfide (MoS₂) and ultrathin hafnium–zirconium oxide (HfZrO₂)-based materials.
The research team also proposed future directions involving the integration of high-speed AFM with artificial intelligence (AI), enabling rapid interpretation of complex nanoscale structures that are difficult for humans to analyze manually, as well as more efficient design of advanced materials.
< Research Image (AI-Generated Image) >
Professor Seungbum Hong stated, “This review shows that atomic force microscopy has evolved beyond a simple observation tool into a key process technology for designing and precisely controlling advanced materials,” adding, “Analytical techniques combined with artificial intelligence will play a critical role in securing technological competitiveness in next-generation semiconductor and energy materials.”
This review was led by Yeongyu Kim (Doctoral student) and Kunwoo Park (integrated MS–PhD program student), both from the Department of Materials Science and Engineering at KAIST, as co-first authors. The research was recognized for its excellence and published as a front cover article in the international journal Journal of Materials Chemistry C, published by the Royal Society of Chemistry, on February 26.
※ Paper title: “Atomic Force Microscopy for Ferroelectric Materials Research”
DOI: https://pubs.rsc.org/en/content/articlehtml/2026/tc/d5tc03998c
< Front Cover Selection Image for Journal of Materials Chemistry C (JMCC) >
This work was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the project on developing an AI platform for multi-scale data-integrated lithium secondary battery design, and has been recognized as establishing a new milestone in the field of nanomaterials.
2026.04.08 View 494 -
Era of Ultra-Slim, Wide Field-of-View and , High-Resolution Cameras Opens with Natural Vision Principles
<(From left) Young-Gil Cha, Hyun-Kyung Kim, Jae-Myeong Kwon, Professor Ki-Hun Jeong, (Top right) Professor Min H. Kim>
A breakthrough technology has emerged to fundamentally solve the "camera protrusion/thickness issue," which has been a persistent limitation as smart devices become thinner. KAIST research team has developed an ultra-thin camera that achieves a wide 140-degree field of view (FOV) without any lens protrusion. This technology is expected to be applied across various fields, including medical endoscopes, wearable devices, and micro-robots.
On the 7th, a joint research team led by Professor Ki-Hun Jeong from the Department of Bio and Brain Engineering and Professor Min H. Kim from the School of Computing announced the development of a "wide-angle biomimetic camera." Inspired by insect vision, the camera is exceptionally thin yet boasts a vast field of view. The team successfully secured a diagonal FOV of 140 degrees—surpassing human peripheral vision—within an ultra-thin structure of less than 1 mm, roughly the thickness of a coin.
High-performance wide-angle cameras typically require multiple stacked lenses, inevitably leading to increased thickness. To overcome this, the research team focused on the visual structure of the parasitic insect Xenos peckii.
<Conceptual diagram of the camera structure mimicking insect compound eye principles and photos of the manufactured ultra-thin camera>
While typical insect compound eyes offer a wide FOV, they suffer from low resolution. Conversely, single-lens cameras provide high resolution but limited FOV. Xenos peckii, however, possesses a unique visual system where multiple eyes capture partial segments of a scene, which the brain then integrates into a single high-resolution image. By introducing this "split-capture and integration" principle into the camera architecture, the team simultaneously achieved both thinness and high image quality. This overcomes the low-resolution issues of conventional compound eye cameras and the narrow FOV limits of single-lens systems.
<Result of reconstructing a single scene by combining partial images captured via a microlens array>
The team implemented a method where several micro-lenses with ellipsoidal shape capture different directions simultaneously, merging them into one sharp image without optical aberration. Notably, by precisely adjusting the lens shape and light entry points, they prevented blurring at the edges of the frame. As a result, uniform clarity is maintained from the center to the periphery, enabling stable imaging even at very close ranges.
With a thickness of only 0.94 mm, this ultra-thin camera is expected to bring innovation to space-constrained fields. It can significantly enhance image acquisition efficiency for medical endoscopes requiring precise observation of narrow areas, as well as for micro-robots and wearable healthcare equipment. This technology shifts the design paradigm from increasing device size for better performance to enabling high-performance imaging in ultra-small form factors.
<Results of photographing actual subjects at close range: microfluidic channels (20 mm distance), oral models (30 mm), and human faces (50 mm)>
Furthermore, the research team has completed a technology transfer to MicroPix Co., Ltd., a specialist in optical imaging, with the goal of full-scale commercialization by next year.
"Conventional wide-angle cameras faced a trade-off where reducing size lowered resolution, and increasing resolution enlarged the device," explained Professor Ki-Hun Jeong. "By applying visual principles from nature, we have secured both a wide FOV and stable image quality in an ultra-compact structure. This is a new image acquisition method usable even in extreme space-constrained environments."
Jae-Myeong Kwon, Ph.D candidate at KAIST, participated as the lead author. The study was published on March 23 in the world-renowned academic journal Nature Communications.
Paper Title: Biologically inspired microlens array camera for high-resolution wide field-of-view imaging
DOI: https://doi.org/10.1038/s41467-026-70967-2
Authors: Jae-Myeong Kwon, Yejoon Kwon, Young-Gil Cha, Dong Hyun Han, Hyun-Kyung Kim, Je-Kyun Park, Min H. Kim & Ki-Hun Jeong
This research was conducted with support from the Mid-Career Researcher Program of the National Research Foundation of Korea (Ministry of Science and ICT), the Korean ARPA-H Project (Ministry of Health and Welfare), and the Materials and Components Technology Development Program (Ministry of Trade, Industry and Energy).
2026.04.07 View 689 -
KAIST Achieves 3-fold Increase in Hydrogen Production Using “High-Entropy” Design—More Mixing, More Strength
<(From Left) Professor Kang Taek Lee, Ph.D candidate Seeun Oh, Researcher Incheol Jeong, Dr. Dongyeon Kim, Ph.D candidate Hyeonggeun Kim>
While mixing materials typically leads to instability, there exists a phenomenon known as “high entropy,” where increasing compositional complexity can actually enhance stability. KAIST researchers leveraged this principle to enable faster proton transport and more efficient reactions within electrochemical cells, developing a technology that significantly improves hydrogen production efficiency. This breakthrough is expected to reduce hydrogen costs and accelerate the transition to clean energy.
KAIST (President Kwang Hyung Lee) announced on the 5th of April that a research team led by Professor Kang Taek Lee from the Department of Mechanical Engineering has developed a novel oxygen electrode material that dramatically improves reaction kinetics and power performance through entropy-maximized design. The oxygen electrode is a key component in electrochemical cells where oxygen evolution occurs during hydrogen production.
Green hydrogen—produced from water without carbon emissions—is considered a cornerstone of future clean energy systems. In particular, protonic ceramic electrochemical cells (PCECs), which generate hydrogen by splitting water using electrical energy while protons migrate through the cell, have attracted attention for their high efficiency. However, their performance has been limited by slow reaction kinetics at the oxygen electrode.
To address this issue, the research team adopted a high-entropy strategy, introducing multiple metal elements simultaneously to increase configurational disorder. Although mixing many elements typically destabilizes structures, under certain compositions, maximizing entropy can instead stabilize a single-phase structure.
<Structural and chemical characterization of PBSCF and PLNNCBSCF. XRD patterns of a) the synthesized PBSCF and PLNNCBSCF and b) enlarged view of the XRD patterns from 31.5 to 33.5°. c) Rietveld refinement results of the XRD profile for PLNNCBSCF, with the inset showing the idealized structure. d) HR-TEM image of PLNNCBSCF with the inset showing lattice fringes. e) Corresponding EDS mappings of the PLNNCBSCF elements. XPS of F) survey peak, G) Pr 3d, and H) O 1s spectra for PBSCF and PLNNCBSCF>
Based on this concept, the researchers designed a high-entropy double perovskite oxygen electrode by incorporating seven different metal elements (Pr, La, Na, Nd, Ca, Ba, Sr) into the A-site of the electrode structure. This material combines a perovskite crystal framework with a double perovskite structure, further enhanced by high-entropy design.
The presence of multiple mixed metal elements improves charge transport and oxygen-related reactions within the electrode, resulting in significantly faster electrochemical reactions for both electricity generation and hydrogen production.
Notably, density functional theory (DFT) calculations revealed that the energy required to form oxygen vacancies—active sites where reactions occur—was reduced by more than 60% compared to conventional materials. This indicates that reactive sites can form more easily and in greater abundance.
Additionally, time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis showed that proton transport speed increased by more than sevenfold, demonstrating that hydrogen generation processes proceed much more efficiently within the electrode.
The performance improvements were substantial. Cells incorporating the new electrode achieved a power density of 1.77 W cm⁻² at 650°C, approximately 2.6 times higher than conventional systems. Hydrogen production performance also improved by approximately threefold (4.42 A cm⁻²) under the same conditions.
Moreover, in long-term testing under steam conditions for 500 hours, performance degradation was only 0.76%, confirming excellent durability and stability over extended operation.
Professor Kang Taek Lee stated, “This study demonstrates that the thermodynamic concept of entropy can be used to control electrode reactivity,” adding, “It has the potential to significantly enhance green hydrogen production efficiency and accelerate the commercialization of the hydrogen economy.”
This study was co-led by Seeun Oh of the Department of Mechanical Engineering at KAIST and Incheol Jeong of the Korea Institute of Geoscience and Mineral Resources. The findings were published on December 16, 2025, in the international journal Advanced Energy Materials (IF: 26.0) and were selected as a front cover article, highlighting their scientific impact.
※ Paper title: “Unveiling Entropy-Driven Performance Enhancement in Double Perovskite Oxygen Electrodes for Protonic Ceramic Electrochemical Cells,” DOI: https://doi.org/10.1002/aenm.202503176※ Authors: Seeun Oh (KAIST, first author), Incheol Jeong (Korea Institute of Geoscience and Mineral Resources, first author), Dongyeon Kim (second author), Hyeonggeun Kim (second author), Kang Taek Lee (corresponding author)
This research was supported by the Mid-Career Researcher Program and the Global Basic Research Laboratory Program funded by the Ministry of Science and ICT (MSIT), Korea.
2026.04.06 View 352 -
Professor Jihyeon Yeom Selected as Early Career Advisory Board Member for Top Chemistry and Materials Journal
< Professor Jihyeon Yeom >
KAIST announced on the 13th that Professor Jihyeon Yeom from the Department of Materials Science and Engineering has been selected as a member of the Early Career Advisory Board (ECAB) for Chemical Reviews, widely considered the world's most prestigious academic journal in the field of chemistry.
Published by the American Chemical Society (ACS), Chemical Reviews is a flagship review journal that comprehensively organizes and surveys the most influential research achievements across all areas of chemistry and materials science. It is evaluated as a top-tier international journal in the field.
The journal boasts an Impact Factor (IF) of 56, ranking it among the highest of all scientific journals worldwide. Its authority is particularly significant because it is a review journal that analyzes global research trends to suggest future academic directions, rather than simply publishing individual experimental data.
The ECAB, which began its term in January 2026, consists of 10 researchers selected from among rising global science leaders. Candidates are evaluated based on academic originality, research impact, and contributions to the scientific community. Members provide advisory roles for the journal's academic direction and strategic planning, contributing to the discovery of next-generation research trends and the expansion of global research networks.
This selection highlights that Professor Yeom’s research achievements are receiving high international acclaim.
Professor Yeom is conducting research on applying "chirality"—a property where objects, like DNA or proteins, are mirror images of each other but cannot be perfectly superimposed—to nanomaterials. Her core work involves precisely controlling atomic arrangements to realize artificial materials that can interact naturally with biological signals.
In particular, she is gaining attention for developing next-generation smart healthcare technology that combines light-responsive chiral materials with Artificial Intelligence (AI) to detect and analyze minute changes in the human body in real time. Professor Yeom explained that these chiral characteristics offer new possibilities for expanding information transmission and processing capabilities beyond simple structural properties.
Building on this foundation, she plans to expand her research into various fields, including precision medical diagnostic technology, next-generation optoelectronic devices utilizing circularly polarized light, and AI-based platforms.
Professor Yeom has established herself as a global leader in chiral materials research, recently publishing results in world-renowned journals such as Nature Communications, Advanced Materials, ACS Nano, and Accounts of Chemical Research.
"Chirality is not just a structural characteristic, but a new degree of freedom that expands the functional and information-processing capabilities of matter," said Professor Yeom. "I plan to expand my research into chiral-based electronic and optical devices, bio-diagnostic technologies, and AI-based spectroscopic platforms in the future."
This ECAB selection once again demonstrates the research competitiveness and international standing of the KAIST Department of Materials Science and Engineering. It is expected to further strengthen KAIST's role as a global research hub in the field of next-generation materials research.
2026.03.13 View 1201 -
KAIST Proposes AI-Driven Strategy to Solve Long-Standing Mystery of Gene Function
<(From Left) Distinguisehd Professor Sang Yup Lee, Dr. Gi Bae Kim, Professor Bernhard O. Palsson>
“We know the genes, but not their functions.” To resolve this long-standing bottleneck in microbial research, a joint research team has proposed a cutting-edge research strategy that leverages Artificial Intelligence (AI) to drastically accelerate the discovery of microbial gene functions.
KAIST announced on January 12th that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bernhard Palsson from the Department of Bioengineering at UCSD, has published a comprehensive review paper. The study systematically analyzes and organizes the latest AI-based research approaches aimed at revolutionizing the speed of gene function discovery.
Since the early 2000s, when whole-genome sequencing became a reality, there were high expectations that the genetic blueprint of life would be fully decoded. However, even twenty years later, the roles of a significant portion of genes within microbial genomes remain unknown.
While various experimental methods—such as gene deletion, analysis of gene expression profiles, and in vitro activity assays—have been employed, discovering gene functions remains a time-consuming and costly endeavor. This is primarily due to the limitations of large-scale experimentation, complex biological interactions, and the discrepancy between laboratory results and actual in vivo responses.
To overcome these hurdles, the research team emphasized that an AI-driven approach combining computational biology with experimental biology is essential.
In this paper, the team provides a comprehensive overview of computational biology approaches that have facilitated gene function discovery, ranging from traditional sequence similarity analysis to the latest deep-learning-based AI models.
Notably, 3D protein structure prediction technologies such as AlphaFold (developed by Google DeepMind) and RoseTTAFold (developed by the University of Washington) have opened new doors. These tools go beyond simple functional estimation, offering the potential to understand the underlying mechanisms of how gene functions operate. Furthermore, generative AI is now extending research boundaries toward designing proteins with specifically desired functions.
Focusing on transcription factors (proteins that act as genetic switches) and enzymes (proteins that catalyze chemical reactions), the team presented various application cases and future research directions that integrate gene sequence analysis, protein structure prediction, and diverse metagenomic analyses.
<Schematic illustration of computational biology methods for enzyme function prediction>
2026.01.12 View 2046 -
AI-Engineered "Nasal Spray Antiviral Platform" Developed to Block Flu and COVID-19
<(From Left) Professor Hyun Jung Chung, Professor Ho Min Kim, Professor Ji Eun Oh>
<(From Left) Dr. Seungju Yang, Dr. Jeongwon Yun, Ph.D candidate Jae Hyuk Kwon>
Respiratory viruses that have diverse strains and mutate rapidly, such as influenza and COVID-19, are difficult to block perfectly with vaccines alone. To solve this problem, KAIST's research team has successfully developed a nasal (intranasal) antiviral platform using AI technology to overcome the existing limitations of interferon-lambda treatments—namely, being "weak against heat and disappearing quickly from the nasal mucosa."
KAIST announced on December 15th that a joint research team—consisting of Professor Ho Min Ktim and Professor Hyun Jung Chung from the Department of Biological Sciences, and Professor Ji Eun Oh from the Graduate School of Medical Science and Engineering used AI to stably redesign the interferon-lambda protein and combined it with a delivery technology that ensures effective diffusion and long-term retention in the nasal mucosa, thereby implementing a universal prevention technology for various respiratory viruses.
Interferon-lambda is an innate immune protein produced by the body to block viral infections, playing a crucial role in stopping respiratory viruses like the common cold, flu, and COVID-19. However, when formulated as a treatment for nasal administration, its actual efficacy was limited by its vulnerability to heat, degrading enzymes, mucus, and ciliary motion.
The research team used AI protein design technology to precisely reinforce the structural weaknesses of interferon-lambda.
First, they significantly increased stability by changing the loose "loop" structures of the protein—which were prone to instability—into rigid "helix" structures that lock in place like a firm spring.
Additionally, to prevent "aggregation" (proteins sticking together to form lumps), they applied "surface engineering" to make the surface more water-compatible. They also introduced "glycoengineering," adding sugar chain (glycan) structures to the protein surface to make it even more robust and stable.
As a result, the newly produced interferon-lambda showed a massive improvement in stability, surviving for two weeks 50℃ and demonstrated the ability to diffuse rapidly even through thick nasal mucus.
The research team further protected the protein by encapsulating it in microscopic "nanoliposomes" and coated the surface with "low-molecular-weight chitosan." This significantly enhanced "mucoadhesion," allowing the treatment to stick to the nasal lining for an extended period.
When this delivery platform was applied to animal models infected with influenza, a powerful inhibitory effect was confirmed, with the virus level in the nasal cavity decreasing by more than 85%.
This technology is a mucosal immune platform that can block viral infections in their early stages simply by spraying it into the nose. It is expected to be a new therapeutic strategy that can respond quickly not only to seasonal flu but also to unexpected new or mutant viruses.
Professor Ho Min Kim stated, "Through AI-based protein design and mucosal delivery technology, we have simultaneously overcome the stability and retention time limitations of existing interferon-lambda treatments. This platform, which is stable at high temperatures and stays in the mucosa for a long time, is an innovative technology that can be used even in developing countries lacking strict cold-chain infrastructure. It also has great scalability for developing various treatments and vaccines." He added, "This is a meaningful achievement resulting from multidisciplinary convergence research, covering everything from AI protein design to drug delivery optimization and immune evaluation through infection models."
This research involved Dr. Jeongwon Yun from the KAIST InnoCORE (AI-Co-Research & Eudcation for innovative Drug Institute, AI-CRED Institute) Dr. Seungju Yang from the Department of Biological Sciences, and PhD student Jae Hyuk Kwon from the Graduate School of Medical Science and Engineering as co-first authors. The results were published consecutively in the renowned international journals Advanced Science (Nov 20) and Biomaterials Research (Nov 21).
Paper 1: Computational Design and Glycoengineering of Interferon-Lambda for Nasal Prophylaxis against Respiratory Viruses, Advanced Science, DOI: 10.1002/advs.202506764
Paper 2: Intranasal Nanoliposomes Delivering Interferon Lambda with Enhanced Mucosal Retention as an Antiviral, Biomaterials Research, DOI: 10.34133/bmr.0287
This research was conducted with support from the KAIST InnoCORE Program, Mid-Career Researcher Support Program and the Bio-Medical Technology Development Program through the National Research Foundation of Korea (NRF), Healthcare Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), the KAIST Convergence Research Institute Operation Program, and the Institute for Basic Science (IBS).
2025.12.15 View 2352 -
KAIST and the World Bank Launch Digital Innovation Initiative to Boost Youth Employment in East Africa
Daejeon, Republic of Korea — November 2025 — KAIST has joined forces with the World Bank to launch a new initiative aimed at advancing youth employment and social protection systems through digital innovation in East Africa. The project, titled “Enhancing Youth Employment Policies through Digital Technologies,” will be implemented in Rwanda, Kenya, and Tanzania over the next three years.
The initiative is jointly led by Professor Kyung Ryul Park of the KAIST Graduate School of Science and Technology Policy, John Van Dyck, Director of the World Bank’s Social Protection and Labor (SPL) Global Practice, and Yoon Young Cho, Senior Economist at the World Bank. Supported by the Korea–World Bank Partnership Facility (KWPF), the project is funded at approximately KRW 1.4 billion (USD 980,000) and will run through 2028.
The collaboration aims to strengthen youth employment and advance the digital transformation of social protection systems in East Africa. In many developing countries, such systems are still managed manually, resulting in inefficiencies and inaccuracies. To address these challenges, the project will establish AI- and big data–driven digital social registry systems that enhance transparency, accuracy, and efficiency in social service delivery.
Beyond technology transfer, the project will also explore broader social and policy challenges that arise in digital labor markets — including algorithmic bias, ethical considerations in AI, and new forms of inequality. Through this work, the partners aim to develop a new model for an “inclusive AI transition,” ensuring that technological innovation contributes to social inclusion and sustainable development. Findings from the project will be published in World Bank reports and policy briefs.
As a global leader in digital governance and data-driven policymaking, South Korea’s experience is expected to play a key role in helping East African governments design and implement resilient, inclusive, and data-based labor and social protection systems.
The KAIST Global Center for Development and Strategy (G-CODEs) will organize two international workshops in collaboration with the Korea Development Institute (KDI), the Ministry of Employment and Labor of Korea, and the Kenya Advanced Institute of Science and Technology (Kenya-AIST). These workshops will help local officials build capacity in applying digital technologies, while providing KAIST researchers and students with hands-on experience in global development cooperation.
A kickoff workshop was held during the World Bank Annual Meetings earlier this month, with participation from Professors Kyung-Ryul Park and Seok-Kyun Woo (Graduate School of Science and Technology Policy), Dean Ji-Yong Eom (Graduate School of Green Growth and Sustainability), Researcher Seung-Hyun Kim, and Consultant Ji-Su Sim (M.S. Class of 2025, STP).
“This collaboration is not merely a technical project but an innovative effort to digitally connect youth employment and social protection systems,” said John Van Dyck, Director of the World Bank SPL Global Practice. “It will help East African governments design sustainable and inclusive digital labor infrastructures.”
Yoon Young Cho, Senior Economist at the World Bank, added, “The project seeks to digitalize social protection systems in East Africa to promote youth employment and social inclusion, focusing on building sustainable, government-led public digital solutions.”
Professor Kyung-Ryul Park of KAIST stated, “Through this partnership with the World Bank, we hope to support inclusive development in East Africa while offering KAIST researchers and students valuable opportunities to learn and grow through real-world international cooperation.”
2025.11.05 View 3321 -
KAIST Develops an AI Semiconductor Brain Combining Transformer's Intelligence and Mamba's Efficiency
<(From Left) Ph.D candidate Seongryong Oh, Ph.D candidate Yoonsung Kim, Ph.D candidate Wonung Kim, Ph.D candidate Yubin Lee, M.S candidate Jiyong Jung, Professor Jongse Park, Professor Divya Mahajan, Professor Chang Hyun Park>
As recent Artificial Intelligence (AI) models’ capacity to understand and process long, complex sentences grows, the necessity for new semiconductor technologies that can simultaneously boost computation speed and memory efficiency is increasing. Amidst this, a joint research team featuring KAIST researchers and international collaborators has successfully developed a core AI semiconductor 'brain' technology based on a hybrid Transformer and Mamba structure, which was implemented for the first time in the world in a form capable of direct computation inside the memory, resulting in a four-fold increase in the inference speed of Large Language Models (LLMs) and a 2.2-fold reduction in power consumption.
KAIST (President Kwang Hyung Lee) announced on the 17th of October that the research team led by Professor Jongse Park from KAIST School of Computing, in collaboration with Georgia Institute of Technology in the United States and Uppsala University in Sweden, developed 'PIMBA,' a core technology based on 'AI Memory Semiconductor (PIM, Processing-in-Memory),' which acts as the brain for next-generation AI models.
Currently, LLMs such as ChatGPT, GPT-4, Claude, Gemini, and Llama operate based on the 'Transformer' brain structure, which sees all of the words simultaneously. Consequently, as the AI model grows and the processed sentences become longer, the computational load and memory requirements surge, leading to speed reductions and high energy consumption as major issues.
To overcome these problems with Transformer, the recently proposed sequential memory-based 'Mamba' structure introduced a method for processing information over time, increasing efficiency. However, memory bottlenecks and power consumption limits still remained.
Professor Park Jongse's research team designed 'PIMBA,' a new semiconductor structure that directly performs computations inside the memory in order to maximize the performance of the 'Transformer–Mamba Hybrid Model,' which combines the advantages of both Transformer and Mamba.
While existing GPU-based systems move data out of the memory to perform computations, PIMBA performs calculations directly within the storage device without moving the data. This minimizes data movement time and significantly reduces power consumption.
<Analysis of Post-Transformer Models and Proposal of a Problem-Solving Acceleration System>
As a result, PIMBA showed up to a 4.1-fold improvement in processing performance and an average 2.2-fold decrease in energy consumption compared to existing GPU systems.
The research outcome is scheduled to be presented on October 20th at the '58th International Symposium on Microarchitecture (MICRO 2025),' a globally renowned computer architecture conference that will be held in Seoul. It was previously recognized for its excellence by winning the Gold Prize at the '31st Samsung Humantech Paper Award.' ※Paper Title: Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving, DOI: 10.1145/3725843.3756121
This research was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP), the AI Semiconductor Graduate School Support Project, and the ICT R&D Program of the Ministry of Science and ICT and the IITP, with assistance from the Electronics and Telecommunications Research Institute (ETRI). The EDA tools were supported by IDEC (the IC Design Education Center).
2025.10.17 View 3745 -
Chemobiological Platform Enables Renewable Conversion of Sugars into Core Aromatic Hydrocarbons of Petroleum
<(From Left) Professor Sun Kyu Han, Ph.D candidate Tae Wan Kim, Professor Kyeong Rok Choi, Professor Sang Yup Lee>
With growing concerns over fossil fuel depletion and the environmental impacts of petrochemical production, scientists are actively exploring renewable strategies to produce essential industrial chemicals. A collaborative research team—led by Distinguished Professor Sang Yup Lee, Senior Vice President for Research, from the Department of Chemical and Biomolecular Engineering, together with Professor Sunkyu Han from the Department of Chemistry at the Korea Advanced Institute of Science and Technology (KAIST)—has developed an integrated chemobiological platform that converts renewable carbon sources such as glucose and glycerol into oxygenated precursors, which are subsequently deoxygenated in the same solvent system to yield benzene, toluene, ethylbenzene, and p-xylene (BTEX), which are fundamental aromatic hydrocarbons used in fuels, polymers, and consumer products.
<Figure 1. Schematic representation of the chemobiological synthesis of BTEX from glucose or glycerol in Escherichia coli>
From Sugars to Aromatic Hydrocarbons of Petroleum
The researchers designed four metabolically engineered strains of Escherichia coli, each programmed to produce a specific oxygenated precursor—phenol, benzyl alcohol, 2-phenylethanol, or 2,5-xylenol. These intermediates are generated through tailored genetic modifications, such as deletion of feedback-regulated enzymes, overexpression of pathway-specific genes, and introduction of heterologous enzymes to expand metabolic capabilities.
During fermentation, the products were continuously extracted into the organic solvent isopropyl myristate (IPM). Acting as a dual-function solvent, IPM not only mitigated the toxic effects of aromatic compounds on cell growth but also served directly as the reaction medium for downstream chemical upgrading. By eliminating the need for intermediate purification, solvent exchange, or distillation, this solvent-integrated system streamlined the conversion of renewable feedstocks into valuable aromatics.
Overcoming Chemical Barriers in An Unconventional Solvent
A central innovation of this work lies in adapting chemical deoxygenation reactions to function efficiently within IPM—a solvent rarely used in organic synthesis. Traditional catalysts and reagents often proved ineffective under these conditions due to solubility limitations or incompatibility with biologically derived impurities.
Through systematic optimization, the team established mild and selective catalytic strategies compatible with IPM. For example, phenol was successfully deoxygenated to benzene in up to 85% yield using a palladium-based catalytic system, while benzyl alcohol was efficiently converted to toluene after activated charcoal pretreatment of the IPM extract. More challenging transformations, such as converting 2-phenylethanol to ethylbenzene, were achieved through a mesylation–reduction sequence adapted to the IPM phase. Likewise, 2,5-xylenol derived from glycerol was converted to p-xylene in 62% yield via a two-step reaction, completing the renewable synthesis of the full BTEX spectrum.
A Sustainable, Modular Framework
Beyond producing BTEX, the study establishes a generalizable framework for integrating microbial biosynthesis with chemical transformations in a continuous solvent environment. This modular approach reduces energy demand, minimizes solvent waste, and enables process intensification—key factors for scaling up renewable chemical production.
The high boiling point of IPM (>300 °C) simplifies product recovery, as BTEX compounds can be isolated by fractional distillation while the solvent is readily recycled. Such a design is consistent with the principles of green chemistry and the circular economy, providing a practical alternative to fossil-based petrochemical processes.
Toward A Carbon-Neutral Future
Dr. Xuan Zou, the first author of this paper, explaind, “By coupling the selectivity of microbial metabolism with the efficiency of chemical catalysis, this platform establishes a renewable pathway to some of the most widely used building blocks in the chemical industry. Future efforts will focus on optimizing metabolic fluxes, extending the platform to additional aromatic targets, and adopting greener catalytic systems.”
In addition, Distinguished Professor Sang Yup Lee noted “As the global demand for BTEX and related chemicals continues to grow, this innovation provides both a scientific and industrial foundation for reducing reliance on petroleum-based processes. It marks an important step toward lowering the carbon footprint of the fuel and chemical sectors while ensuring a sustainable supply of essential aromatic hydrocarbons.”
This research was supported by the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries Project (2022M3J5A1056117) and the Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry Project (RS-2024-00399424), funded by the National Research Foundation supported by the Korean Ministry of Science and ICT. This study was published in the latest issue of the Proceedings of the National Academy of Sciences of the United States of America (PNAS).
2025.10.13 View 2849 -
3D Printing Becomes Stronger and More Economical with Light and AI
<(Front) Ph.D. candidate Jisoo Nam, (Back row, from left) Ph.D. candidate Boxin Chen, Professor Miso Kim>
Photocurable 3D printing, widely used for everything from dental treatments to complex prototype manufacturing, is fast and precise but has the limitation of being fragile and easily broken by impact. A KAIST research team has developed a new technology to overcome this weakness, paving the way for the more robust and economical production of everything from medical implants to precision machine parts.
KAIST (President Kwang Hyung Lee) announced on the 29th that Professor Miso Kim's research team in the Department of Mechanical Engineering has developed a new technology that fundamentally resolves the durability limitations of photocurable 3D printing.
Digital Light Processing (DLP)-based 3D printing is a technique that uses light to solidify liquid resin (polymer) to rapidly manufacture precise structures, used in various fields such as dentistry and precision machinery. While traditional injection molding offers excellent durability, it requires significant time and cost for mold fabrication. In contrast, photocurable 3D printing allows for flexible shape realization but has a durability drawback.
Professor Kim's team solved this problem by combining two key elements:
A new photocurable resin material that absorbs shock and vibration while allowing for a wide range of properties from rubber to plastic.
A machine learning-based design technology that automatically assigns optimal strength to each part of the structure.
<Figure 1. Schematic of a new manufacturing technology for high-durability photocurable 3D printing using light-controlled gradient structures. This approach integrates the development of stiffness-controllable viscoelastic polyurethane acrylate (PUA) materials, machine learning-based property gradient optimization, and grayscale DLP 3D printing. The technology enhances damping performance and alleviates stress concentration, providing an integrated solution for high reliability, durability, and customized manufacturing. It demonstrates potential applications in structural components subjected to repetitive loads such as joints, automotive interior parts, and precision machinery components>
The research team developed a Polyurethane Acrylate (PUA) material incorporating dynamic bonds, which significantly increases shock and vibration absorption capability compared to existing materials. Furthermore, they successfully applied 'grayscale DLP' technology, which controls the light intensity to achieve different strengths from a single resin composition, thereby assigning customized strength to specific areas within the structure. This concept is inspired by the harmonious and different roles played by bone and cartilage in the human body.
A machine learning algorithm automatically proposes the optimal strength distribution by analyzing the structure and load conditions. This organically connects material development and structural design, enabling customized strength distribution.
The economic efficiency is also noteworthy. Previously, expensive 'multi-material printing' technology was required to achieve diverse material properties, but this new technology yields the same effect with a single material and a single process, significantly reducing production costs. It eliminates the need for complex equipment or material management, and the AI-based structural optimization shortens research and development time and product design costs.
Professor Miso Kim explained, "This technology simultaneously expands the degrees of freedom in material properties and structural design. Patient-specific implants will become more durable and comfortable, and precision machine parts can be manufactured more robustly." She added, "The fact that it secures economic viability by realizing various strengths with a single material and single process is highly significant," and "We anticipate its utilization across various industrial fields such as biomedical, aerospace, and robotics."
The research was spearheaded by Professor Miso Kim's team at the KAIST Department of Mechanical Engineering, with Ph.D. candidate Jisoo Nam as the first author. Boxin Chen, a student from Sungkyunkwan University, also contributed to the collaborative research. The findings were published online on July 16 in the world-renowned journal in materials science, Advanced Materials (IF 26.8). Recognizing the research's excellence, it was also selected for the journal's Frontispiece.
Paper Title: Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials
DOI: 10.1002/adma.202504075
The achievements of this research have brought Professor Miso Kim significant international attention, as she simultaneously received the 'Wiley Rising Star Award' and the 'Wiley Women in Materials Science Award' in July 2025, hosted by the international academic publisher Wiley.
The Wiley Rising Star Award is given to emerging researchers with the potential for academic leadership, and the Wiley Women in Materials Science Award is a prestigious honor established to celebrate outstanding female scientists in the field of materials science.
<Figure 2. Frontispiece image (scheduled for Issue 42). Multi-property structure fabricated using a photocurable 3D printer. By varying the projector light intensity by location, stronger light creates rigid regions while weaker light forms flexible ones. AI designs an optimized pattern for the structural shape to prevent fracture and reinforce the overall strength.>
This research was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Nos. NRF-2021R1A2C2095767, RS-2023-00254689, and RS-2024-00433654).
2025.09.30 View 3553 -
Professor Jae-woong Jeong Wins September's Scientist and Engineer of the Month Award
<Professor Jae-Woong Jeong from Department of Electrical and Electronic Engineering>
The Ministry of Science and ICT and the National Research Foundation of Korea have announced that Professor Jae-Woong Jeong from KAIST Department of Electrical and Electronic Engineering has been selected as the September recipient of the "Scientist of the Month" award.
The "Scientist of the Month" award recognizes researchers who have made a significant contribution to the development of science and technology by creating unique R&D achievements over the past three years. The award is given to one person each month and includes a commendation from the Minister of Science and ICT and a 10 million KRW prize, funded by the Science and Technology Promotion Fund/Lottery Fund of the Ministry of Science and ICT. In the lead-up to "World Patient Safety Day (September 17)," the Ministry of Science and ICT and the National Research Foundation selected Professor Jeong Jae-Woong as the award recipient for his contribution to healthcare innovation through convergence research on wearable and implantable electronic devices and medical instruments, including the development of an intravenous (IV) needle that softens in response to body temperature to enhance patient safety.
Intravenous injection is a treatment method that involves directly injecting medication into a blood vessel. It is widely used in the medical field due to its ability to provide rapid and continuous drug effects. However, conventional IV needles, made of rigid metal or plastic, can damage blood vessel walls or cause complications like phlebitis. Furthermore, there is a risk of needle-stick injuries and subsequent disease transmission for medical professionals during the disposal process.
Professor Jae-Woong Jeong developed a variable-stiffness* needle that is rigid at room temperature but softens like biological tissue when inserted into the body. This innovation utilizes the unique property of the liquid metal gallium, which changes from a solid to a liquid phase in response to body temperature. * Variable-stiffness: The characteristic of being able to adjust the level of rigidity (stiffness) according to a situation or condition.
The variable-stiffness needle not only ensures a patient's free movement but also maintains a soft state at room temperature after use, preventing needle-stick accidents for medical professionals and fundamentally eliminating the issue of unethical needle reuse.
< An intravenous needle that softens with body temperature. Intravenous injection is a treatment method that involves directly injecting medication into a blood vessel, which allows for a rapid and continuous supply of drugs, making it a globally accepted form of patient care. This research utilized the property of liquid metal gallium, which changes from a solid to a liquid state in response to body temperature, to develop a variable-stiffness intravenous needle that is rigid but softens like tissue upon insertion into the body. This needle allows for stable drug delivery without damaging blood vessels, even when the patient moves. Furthermore, the irreversible softening due to the supercooling phenomenon of gallium can fundamentally prevent post-use needle-stick injuries or unethical reuse, contributing to the safety of both patients and medical staff. This variable-stiffness technology is expected to be widely utilized in the implementation of various wearable and implantable devices that can change their properties according to different situations and purposes. >
Furthermore, Professor Jae-woong Jung focused on the phenomenon in which the temperature of surrounding tissue decreases when a drug leaks during intravenous (IV) injection. He developed a function that enables real-time monitoring of local body temperature by integrating a nanofilm temperature sensor into an IV needle, thereby allowing real-time detection of IV drug leakage.
This research achievement, which presents a new vision for promoting patient health and ensuring medical staff safety as required by the World Health Organization (WHO), was published as the cover article of the international journal Nature Biomedical Engineering in August 2024.
Professor Jae-Woong Jeong stated, “This research is highly significant as it proposes a way to overcome the problems caused by conventional rigid medical needles and solves the infection risks from needle-stick injuries or reuse.” He added, “I will continue to dedicate my efforts to R&D so that variable-stiffness needle technology can evolve into a core technology in the medical field, enhancing the safety of both patients and medical professionals.
To provide more robust support to researchers who lead such outstanding achievements, the Ministry of Science and ICT has prepared a record-high R&D budget of 11.8 trillion KRW (government proposal), including the Life Sciences (Bio) Medical Technology Development Project (361.1 billion KRW in '25 → 434.3 billion KRW in '26, proposed). The Ministry plans to strengthen investment in future industries, such as advanced life sciences, and will further reinforce rewards and recognition for researchers who produce excellent results to foster a researcher-centric R&D ecosystem.
2025.09.05 View 2761 -
KAIST Unlocks the Secret of Next-Generation Memory
<(From Left) Professor Sang-Hee Ko Park, Ph.D candidate Sunghwan Park, Ph.D candidate Chaewon Gong, Professor Seungbum Hong>
Resistive Random Access Memory (ReRAM), which is based on oxide materials, is gaining attention as a next-generation memory and neuromorphic computing device. Its fast speeds, data retention ability, and simple structure make it a promising candidate to replace existing memory technologies. KAIST researchers have now clarified the operating principle of this memory, which is expected to provide a key clue for the development of high-performance, high-reliability next-generation memory.
KAIST (President Kwang Hyung Lee) announced on the 2nd of September that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with a research team led by Professor Sang-Hee Ko Park from the same department, has for the first time in the world precisely clarified the operating principle of an oxide-based memory device, which is drawing attention as a core technology for next-generation semiconductors.
Using a 'Multi-modal Scanning Probe Microscope (Multi-modal SPM)' that combines several types of microscopes*, the research team succeeded in simultaneously observing the electron flow channels inside the oxide thin film, the movement of oxygen ions, and changes in surface potential (the distribution of charge on the material's surface). Through this, they clarified the correlation between how current changes and how oxygen defects change during the process of writing and erasing information in the memory.
*Several types of microscopes: Conductive atomic force microscopy (C-AFM) for observing current flow, electrochemical strain microscopy (ESM) for observing oxygen ion movement, and Kelvin probe force microscopy (KPFM) for observing potential changes.
With this special equipment, the research team directly implemented the process of writing and erasing information in the memory by applying an electrical signal to a titanium dioxide (TiO2) thin film, confirming at the nano-level that the reason for the current changes was the variation in the distribution of oxygen defects.
In this process, they confirmed that the current flow changes depending on the amount and location of oxygen defects. For example, when there are more oxygen defects, the electron pathway widens, and the current flows well, but conversely, when they scatter, the current is blocked. Through this, they succeeded in precisely visualizing that the distribution of oxygen defects within the oxide determines the on/off state of the memory.
<Overview of the Research Process. By using one of the SPM modes, C-AFM (Conductive Atomic Force Microscopy), resistive switching corresponding to the electroforming and reset processes is induced in a 10 nm-thick TiO₂ thin film, and the resulting local current variations caused by the applied electric field are observed. Subsequently, at the same location, ESM (Electrochemical Strain Microscopy) and KPFM (Kelvin Probe Force Microscopy) signals are comprehensively analyzed to investigate and interpret the spatial correlation of ion-electronic behaviors that influence the resistive switching phenomenon>
This research was not limited to the distribution at a single point but comprehensively analyzed the changes in current flow, the movement of oxygen ions, and the surface potential distribution after applying an electrical signal over a wide area of several square micrometers (µm2). As a result, they clarified that the process of the memory's resistance changing is not solely due to oxygen defects but is also closely intertwined with the movement of electrons (electronic behavior).
In particular, the research team confirmed that when oxygen ions are injected during the 'erasing process (reset process)', the memory can stably maintain its off state (high resistance state) for a long time. This is a core principle for increasing the reliability of memory devices and is expected to provide an important clue for the future development of stable, next-generation non-volatile memory.
Professor Seungbum Hong of KAIST, who led the research, said, "This is an example that proves we can directly observe the spatial correlation of oxygen defects, ions, and electrons through a multi-modal microscope." He added, "It is expected that this analysis technique will open a new chapter in the research and development of various metal oxide-based next-generation semiconductor devices in the future."
<By combining C-AFM and ESM techniques, the correlation between local conductivity and variations in oxygen vacancy concentration after resistive switching is analyzed. After the electroforming process, regions with increased conductivity exhibit an enhancement in the ESM amplitude signal, which can be interpreted as an increase in defect ion concentration. Conversely, after the reset process, regions with reduced conductivity show a corresponding decrease in this signal. Through these observations, it is spatially demonstrated that changes in conductivity and local defect ion concentration after resistive switching exhibit a positive correlation>
This research, in which Ph.D. candidate Chaewon Gong from the KAIST Department of Materials Science and Engineering participated as the first author, was published on July 20 in 'ACS Applied Materials and Interfaces', a prestigious academic journal in the field of new materials and chemical engineering published by the American Chemical Society (ACS).
※ Paper Title: Spatially Correlated Oxygen Vacancies, Electrons and Conducting Paths in TiO2 Thin Films
This research was carried out with the support of the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.09.02 View 2788