Kaist

KAIST Develops a Soft 3D-Printed Robotic Hand that..

  < The research team. (From left) Dr. Younghan Song (KIST), Professor Bumsoo Park (Seoul National University of Science and Technology), Professor Seungchul Lee (KAIST), and Dr. Jongbeom Na (KIST) >   3D printers that once could only produce rigid objects can now create products as soft and stretchable as rubber. A team of Korean researchers used AI to identify the optimal "recipe" for a material that can be printed into complex shapes while stretching to more than six times its original length. The material is expected to expand the range of applications for 3D printing, from robotic hands to form-fitting wearable devices and custom medical devices. KAIST (President Choong-Sik Bae) announced on September 1 that a research team led by Professor Seungchul Lee from the Department of Mechanical Engineering, working with Dr. Jongbeom Na's team at the Korea Institute of Science and Technology’s (KIST, President Sang-Rok Oh) Extreme Materials Research Center and Professor Bumsoo Park from the Department of Manufacturing Systems and Design Engineering (MSDE) at Seoul National University of Science and Technology (SEOULTECH, President Dong-Hwan Kim), had used AI to develop a material that is both 3D-printable and highly stretchable, like rubber. The need for such materials — soft, stretchable, and capable of forming complex shapes — has been growing as soft robots that come into direct contact with people, wearable devices worn on the body, and medical devices custom-fitted to patients have drawn increasing attention. The 3D printing technology the team used, Digital Light Processing (DLP), cures a liquid material into a desired shape by exposing it to light. While DLP can quickly produce complex structures, making a material more stretchable and durable tends to raise its viscosity to the point that it no longer flows well enough to be printed. Conversely, thinning the material to make it easier to print reduces its stretchability and strength. Thus, developing a material that is both easy to print and highly stretchable was the central challenge. The team used AI to identify the optimal "material recipe" that satisfies both conditions. Notably, the training data included not only materials that print well, but also highly viscous materials that are difficult to print. The researchers cured various liquid material formulations in small molds and measured how stretchable and hard they were, how quickly they cured under light, and how well they flowed. This produced a dataset linking a wide range of material formulations to their respective properties. < Figure 1. AI-based material design framework proposed in this study and its application to soft robotics >   The team then used machine learning to examine the relationship between material formulation and performance. Based on this, the AI identified the optimal material combination that is both 3D-printable and highly stretchable. The material identified by the AI printed reliably on a DLP 3D printer and showed high stretchability, extending to more than six times its original length when pulled, without easily tearing. To verify its real-world potential, the team 3D-printed a "soft actuator" using the material. A soft actuator is a device that uses air pressure and other means to create gentle, muscle-like movement. When inflated with air, it expanded like a balloon and bent as naturally as a human finger. < Figure 2. AI-based design and mechanical properties of the 3D-printing material >   A soft robotic hand made by combining several actuators lifted a 1 kg water bottle and successfully and stably grasped objects of varying shapes and rigidity, from fragile eggs to glass bottles, an egg carton, and a computer mouse. Beyond developing a single highly stretchable material, this research is significant for presenting an AI-based method for more quickly identifying materials with desired properties. Previously, researchers had to directly formulate and test countless materials to find the optimal combination. Going forward, AI can first identify promising material combinations based on experimental data, which researchers then verify through testing, thereby reducing trial and error and shortening material development time. "This research is significant as it shows that combining researchers' experimental data with artificial intelligence can efficiently identify optimal material combinations that were previously difficult to find," explained Professor Seungchul Lee. "We expect it to be used to more rapidly develop 3D-printing materials with the performance needed across a range of fields, including soft robots, wearable devices, and custom medical devices." < Figure 3. Soft robotic gripper fabricated using the newly developed material >   The study, with Dr. Younghan Song and Professor Bumsoo Park as co-first authors, was published in the international journal Nature Communications on June 4. Paper title: Machine learning guided formulation design of digital light processing printable elastomers beyond viscosity stretchability tradeoff DOI: https://doi.org/10.1038/s41467-026-73735-4 This research was supported by the Ministry of Trade, Industry and Resource's Machinery and Equipment Industry Technology Development Program (20023762), and by the Ministry of Science and ICT's Nano & Material Technology Development Program (RS-2026-25534767) and Excellent New Researcher Program (RS-2024-00350423).  

KAIST Opens the Era of Industrial-Scale Microbial ..

  < Members of the research team, from left: Distinguished Professor Sang Yup Lee, doctoral student Seok Yeong Jung, and Sol Choi, CEO of SilicoBio. >   The question is no longer whether microbial foods can be made. The question now is who can turn them into an industry first. KAIST researchers have comprehensively analyzed the conditions required for the microbial food industry to succeed across manufacturing, markets, and regulation, and have proposed growth strategies for the next-generation protein industry. KAIST (President Choongsik Bae) announced on the 31st of July that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, together with researchers from SilicoBio, a KAIST faculty startup, has comprehensively analyzed the conditions needed for the microbial food industry to succeed in terms of manufacturing, market entry, and regulatory readiness, and has presented an industrialization strategy and roadmap. < [Figure 1] Regional distribution of global microbial food companies and production infrastructure, highlighting gaps in manufacturing capacity >   This study is significant in that it did not develop a new microorganism or production technology, but instead systematically analyzed the key challenges involved in connecting laboratory-based core technologies to real-world industry. In particular, by presenting an integrated perspective that encompasses manufacturing readiness, market entry strategies, and regulatory responses, the study proposes a direction for developing microbial foods beyond the next-generation protein industry into a future biomanufacturing platform. It is expected to serve as an important milestone for strengthening national biomanufacturing competitiveness and fostering the global sustainable food industry. The researchers analyzed that competition in the microbial food industry is shifting from productivity at the laboratory level to manufacturing readiness. They identified stable raw material supply and quality control, control and safety assurance of non-model microorganisms, reduction of downstream processing costs, and regulatory compliance for byproduct recycling as key factors that will determine the pace of commercialization. Manufacturing Readiness refers to the level at which a laboratory technology can be reliably produced at industrial scale. Non-model microorganisms are microorganisms with high industrial potential but insufficient accumulated research infrastructure. Downstream processing refers to the processes of separating, purifying, concentrating, and drying target components after fermentation. < [Figure 2] Predictable regulation—not technology alone—is critical to market entry for microbial foods. This figure analyzes regulatory and policy barriers by comparing the European Union’s Novel Food authorization process with the U.S. GRAS notice procedure. >   The researchers particularly emphasized that future competitiveness will depend less on the excellence of any single technology and more on the ability to build integrated manufacturing platforms. An Integrated Manufacturing Platform refers to a production system that operates the entire process as one connected framework, from strain development and large-scale fermentation to purification, quality control, and product formulation. Even for the same microbial food product, the choice of raw material can affect pretreatment costs and quality variability, while the choice of strain and fermentation process can greatly influence production cost, energy use, and product quality. The researchers therefore concluded that future industrial competitiveness will depend on how quickly companies can build manufacturing platforms that optimize these factors in an integrated way. On the market side, the researchers also identified the conditions needed for the microbial food industry to succeed. Based on consumer surveys and industry cases, they found that microbial foods cannot spread simply by emphasizing environmental sustainability. Consumers place importance on taste, texture, familiarity, and safety, while food manufacturers value functionality that can be applied to actual products. Companies and investors, meanwhile, consider the predictability of regulatory approval procedures and speed of market entry to be especially important. In other words, the microbial food market has entered an industrial stage where not only technology, but also product development capability and regulatory readiness are evaluated together. The researchers also argued that microbial foods should not be viewed merely as an alternative protein industry. They suggested that microbial foods have the potential to develop into a core platform for precision fermentation-based functional food ingredients, high-value biomaterials, and circular biomanufacturing. Precision Fermentation is a technology that uses microorganisms to selectively produce specific proteins or functional substances. Circular Biomanufacturing refers to a sustainable manufacturing system that uses byproducts and renewable resources to produce new bio-based products. This means that microbial foods could become not only a future food source, but also a new production system connecting the global food, materials, and biomanufacturing industries. The industrialization strategy proposed in this study is also closely aligned with the business direction of SilicoBio, which participated in the joint research. Based on the manufacturing readiness strategy presented in the study, SilicoBio is working to build a platform that connects microbial proteins and functional food ingredients to industrial-scale fermentation, scale-up, and product development. Scale-up refers to the process of expanding production from laboratory scale to industrial scale. Distinguished Professor Sang Yup Lee of KAIST said, “As global competition surrounding synthetic biology and biomanufacturing intensifies, microbial foods are growing into a key industry that will shape national biomanufacturing competitiveness beyond future food.” He added, “Going forward, competitiveness will be determined by how quickly we can build an industrialization ecosystem that connects core technologies to real production and markets.” A SilicoBio representative said, “Our goal is to connect the industrialization strategy proposed in this study to actual production and commercialization,” adding, “We will build a platform capable of stably producing microbial-based next-generation foods and functional biomaterials.” < [Figure 3] Microbial foods can expand beyond alternative proteins into a circular biomanufacturing platform. This conceptual diagram illustrates the key conditions for industrializing microbial foods and their potential pathways for expansion. >   This study, with Seok Yeong Jung, a doctoral student in the Department of Chemical and Biomolecular Engineering, as first author and researchers from SilicoBio participating as co-authors, was published on July 17 in the international journal One Earth (Impact Factor 15.3, JCR top 2.07％). Paper title: Microbial foods as scalable platforms toward a circular protein economy for sustainable nutrition DOI: https://doi.org/10.1016/j.oneear.2026.101772 Authors: Sang Yup Lee (KAIST, corresponding author), Seok Yeong Jung (KAIST, first author), Sol Choi (SilicoBio, second author), Jun-Woo Kim (SilicoBio and Inha University, third author), and two others SilicoBio is a KAIST faculty startup founded in June 2025 by Distinguished Professor Sang Yup Lee, a world-renowned scholar in synthetic biology. The company focuses on connecting laboratory-level achievements in systems metabolic engineering to real industrialization. By combining KAIST’s core technologies with the industrialization experience of personnel from CJ BIO, SilicoBio has built a team capable of reviewing not only strain design, but also industrial-scale fermentation and scale-up, material purification and product development, pilot production, and process validation. Based on this foundation, SilicoBio is pursuing a phased commercialization strategy, starting with next-generation protein products and expanding into functional ingredients and eventually new drug and novel material candidates. This research was supported by the “Development of Next-Generation Biorefinery Core Technologies to Lead the Biochemical Industry” project under the Petroleum-Alternative Eco-Friendly Chemical Technology Development Program funded by the Ministry of Science and ICT, and by the “Advancement of a Synthetic Biology-Based Industrial Cell Factory Platform and Commercialization of High-Value Functional Biomaterials” project under the Deep Science Startup Activation Support Program funded by the Commercialization Promotion Agency for R&D Outcome.

KAIST Develops Smartphone-Based Technology to Dete..

  < The research team. From left: Jonghyuk Yun , KAIST Ph.D. student (first author); Sean Rui Xiang Tan , researcher at the National University of Singapore (NUS); and Jun Han, professor in the KAIST School of Computing (corresponding author). >   A smartphone can now be transformed into a “hidden-camera detector.” KAIST researchers have developed an AI technology that can detect hidden cameras using only a smartphone and a low-cost LED device. This new security technology enables users to protect their privacy more easily and is expected to help prevent illegal filming in everyday spaces such as hotels and short-term rentals. KAIST (President Choongsik Bae) announced on August 30 that a research team led by Professor Jun Han of the School of Computing, in collaboration with the National University of Singapore and Singapore Management University, has developed “SweepLED,” a technology that detects hidden cameras by attaching an LED case to a smartphone. < Figure1. Overview of SweepLED. The system scans objects using a smartphone case equipped with an LED array and detects hidden cameras by analyzing the distinctive reflection patterns of camera lenses under changing illumination. >     As hidden cameras are increasingly being installed in everyday spaces such as hotels, short-term rentals, and restrooms, the need is growing for detection technology that everyday users can easily use. However, existing portable detectors require users to visually identify bright reflective spots, which can lead to false positives by mistaking reflections from metal, glass, or glossy plastic surfaces for camera lenses. SweepLED works by keeping the smartphone camera fixed while changing only the direction of the LED illumination, then analyzing the patterns of reflected light that appear on object surfaces. Reflections from ordinary glossy objects tend to move or disappear depending on the direction of the light. In contrast, camera lenses show distinctive deformation patterns in their reflections due to their internal lens, aperture, and sensor structures. The research team uses deep learning-based analysis to distinguish these differences in temporal reflection patterns. While conventional detection methods rely on the user’s eyes to simply look for “bright spots,” SweepLED is different in that it analyzes both the movement and shape changes of reflections across multiple lighting angles. This enables more reliable detection of hidden camera lenses inside various everyday objects commonly found in lodging spaces, such as chargers, clocks, remote controls, and everyday objects. The research team evaluated SweepLED on 30 objects that may be found in real-world environments and found that it achieved approximately 94％ detection accuracy. It also took less than five seconds to inspect a single object. < Figure 2. SweepLED’s hidden-camera detection pipeline. The system identifies potential reflections in the captured video sequence and detects hidden cameras by analyzing reflection patterns across frames. It combines detection results from multiple viewpoints to improve detection reliability. >   In addition, the core components of the LED case attached to the smartphone cost less than USD 7, or about KRW 10,000, demonstrating the potential for this technology to be developed into an affordable detection tool that general users can easily access. Professor Jun Han said, “Hidden cameras pose a serious threat to personal safety and privacy in everyday spaces,” adding, “This research is meaningful in that it combines low-cost smartphone-based hardware with AI analysis to present the possibility of a practical detection technology that even non-experts can use.” < Figure 3. SweepLED hardware prototype and operation. A low-cost PCB-based LED array and a modular smartphone-mounted case enable video capture in real-world environments. >   This paper, with KAIST doctoral student Jonghyuk Yun as first author, was presented on June 20 at ACM MobiSys 2026, one of the leading international conferences in the field of mobile computing. Paper title: Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps https://doi.org/10.1145/3812835.3814866 Author information: Jonghyuk Yun (first author), Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, and Professor Jun Han (corresponding author) This research was supported by the STEAM Global Convergence Research Support Program and the Mid-Career Researcher Program of the Ministry of Science and ICT and the National Research Foundation of Korea.  

Next-Generation Biological Foundation Model, K-Fol..

  < (From left) KAIST Professors Byung-Ha Oh, Gyuri Lee, Sung Ju Hwang, Woo Youn Kim, Sungsoo Ahn, and Ho Min Kim >   “Design a drug candidate that binds effectively to this protein.” In response to such a request, AI predicts the protein’s three-dimensional structure, analyzes which compounds are most likely to bind to it, and designs promising drug candidates. KAIST researchers have developed K-Fold, the world's fastest Bio-AI model for protein structure prediction, which also supports drug candidate design. KAIST (President Choongsik Bae) announced on August 28 that it had formed “Team KAIST” after being selected as the lead institution for the Ministry of Science and ICT’s “AI Specialized Foundation Model Project” and unveiled K-Fold, a next-generation Bio-AI model developed by Team KAIST. Team KAIST is led by Professor Woo Youn Kim from the Department of Chemistry. His research group, together with Professors Sung Ju Hwang and Sungsoo Ahn's groups at the Kim Jaechul Graduate School of AI, developed the AI model. Professors Byung-Ha Oh, Ho Min Kim, and Gyuri Lee from the Department of Biological Sciences oversaw protein data construction and validation. HITS, a KAIST faculty startup, integrated K-Fold into HyperLab, its web-based AI research platform, enabling researchers to use the model in real-world research workflows. In addition, the Korea Pharmaceutical and Bio-Pharma Manufacturers Association (KPBMA) and the Korea Biotechnology Industry Organization (KoreaBIO) will lead efforts to raise awareness of K-Fold’s achievements and promote its use across the industry. K-Fold’s defining capability is its ability to predict the binding between proteins and drug candidates—a critical step in drug discovery. Drug development begins with determining the structure of a disease-related protein and identifying, among numerous compounds, those most likely to bind to the protein and produce the desired effect. K-Fold not only predicts a protein’s three-dimensional structure, but also calculates where and how a drug candidate is likely to bind, helping researchers identify promising candidates more quickly. K-Fold goes beyond predicting the structure of a single protein. It can also predict the structures formed when different biomolecules interact, including protein–protein and protein–drug candidate complexes, as well as complexes involving DNA and RNA. In the project’s stage evaluation conducted in March, its accuracy in predicting molecular complex structures was assessed as approaching that of AlphaFold3, developed by Google DeepMind. In an in-house performance evaluation conducted by the research team in August, K-Fold also outperformed existing global models in selected evaluation categories. K-Fold demonstrated particularly strong performance in predicting how drug candidates bind to and act on key therapeutic targets, including G protein-coupled receptors (GPCRs) and kinases, which are major drug targets for cancer and other diseases. It also performed strongly in targeted protein degradation (TPD), an emerging drug discovery approach designed to directly eliminate disease-causing proteins. K-Fold also significantly increased the speed of structure prediction. Conventional protein structure prediction models often require a complex preprocessing step that searches for and compares large amounts of data on similar proteins before calculating a structure. K-Fold applies a new approach that does not depend on this process, eliminating the need for preprocessing calculations and increasing structure prediction speeds by up to 25 times compared with existing models. This means that researchers can evaluate more drug candidates within the same amount of time. By reducing the time and computing resources required for structure prediction, K-Fold can help rapidly identify the most promising compounds from a vast pool of candidates and narrow the selection for experimental validation. “National competitiveness in the AI era depends on sovereign AI capabilities, which is the crucial ability to develop and deploy core technologies independently,” said KAIST President Choongsik Bae. “K-Fold is significant because it combines homegrown AI technology with biotechnology to challenge the world’s leading technologies and translates that capability into a service applicable to real-world drug discovery. KAIST will continue to strengthen Korea’s technological sovereignty in AI and its future competitiveness by advancing the convergence of foundational AI technologies with science and technology.” The research team went beyond developing K-Fold as a standalone model, turning it into an AI research service that researchers can use through a conversational web-based interface. K-Fold has been integrated into HyperLab, a multi-agent platform developed by HITS, a KAIST faculty startup. This allows researchers to use the model without having to build their own high-performance computing infrastructure or operate complex AI software. For example, if a researcher asks the AI to “design an antibody that binds strongly to this protein,” it provides step-by-step support for predicting the protein’s structure, designing candidates with a high likelihood of binding, and computationally evaluating the results. Researchers can also ask it to “find a suitable peptide candidate for this cancer target protein.” In practical terms, instead of moving between multiple software tools to calculate structures and analyze results, researchers can simply state their research objective and have the AI carry out the necessary analyses and design tasks in sequence. To support these capabilities, HyperLab incorporates approximately 120 computational tools and 160 specialized functions for structure prediction, drug design, and the analysis of life science data, including genomic and proteomic data. It also connects more than 100 specialized databases with a large-scale knowledge graph, enabling the platform to retrieve relevant scientific information and apply it to its analyses. HyperLab aims to serve as an AI Co-Scientist that assists researchers throughout the research process by supporting the full workflow, from understanding a research question and selecting the appropriate tools to predicting structures, analyzing results, and iteratively improving designs. Bio AI is emerging as a critical technology capable of reducing the time and cost required for drug discovery, driving intense competition among global technology companies and major research institutions in the United States, the United Kingdom, and China. The development of K-Fold is significant because it lays the foundation for sovereign bio AI by securing core bio AI technology domestically, rather than relying solely on overseas models, and making it available for real-world research. The achievement was first presented at the 2026 Annual Meeting of the Korean Federation of Biomolecular Science, held on June 23, where Professor Woo Youn Kim from the KAIST Department of Chemistry delivered a keynote lecture titled “Generative Drug Design Powered by Agentic AI.” “K-Fold was developed not to follow existing models, but to overcome the limitations of conventional approaches through a new AI architecture,” explained Professor Kim. “We will develop it into an AI-for-Science platform that makes world-class bio AI technology accessible to researchers everywhere.” The Team KAIST consortium plans to release K-Fold free of charge. HyperLab will provide beta access to researchers in Korea and abroad, and gradually expand its commercial services by the end of this year. Meanwhile, industry training on K-Fold is gaining momentum. An online session hosted by KoreaBIO on August 27 attracted 85 participants, while 118 have registered for KPBMA’s hybrid session on September 1. Designed primarily for researchers and practitioners at pharmaceutical and biotech companies, the training covers how to use K-Fold and presents case studies of its application to drug design. The initiative is intended to accelerate the adoption of sovereign bio-AI across the industry. Related website: [HyperLab Co-Scientist] https://hyperlab.ai/features-co-scientist K-Fold–HyperLab 3.0 Introduction Video: https://drive.google.com/file/d/1MxhZr-C3pQdIVn1EP44hQICj8G70is3_/view This research was supported by the Ministry of Science and ICT (MSIT, PJT-25-100009)

Neural Implant in Korea Remotely Controlled from t..

  < From left, Eun Young Jeong, doctoral student in KAIST >     A researcher in Chicago remotely controls a miniaturized brain implant in Daejeon, Korea — over the internet. Korean researchers have developed a wireless device that can deliver drugs and light to precisely modulate targeted neurons from anywhere in the world. The technology is expected to overcome the constraints of distance and location, supporting long-term studies of brain disorders and the future development of therapeutic devices. KAIST (President Choongsik Bae) announced on August 27 that a research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering, in collaboration with Professor Wha Young Kim's team at Yonsei University College of Medicine, has developed an IoT-enabled wireless neural implant that integrates drug delivery, optical stimulation, wireless communication, and internet-based remote control into a single miniaturized device. < Figure 1. Conceptual diagram of the IoT-based wireless neural implant. >   Conventional studies involving optical stimulation or drug delivery to the brain often required bulky equipment connected by wires, restricting the natural movement of experimental animals. Even wireless devices had their own limitations, often requiring researchers to operate them at close range, thereby restricting experimental flexibility and introducing the so-called “observer effect”. To overcome these limitations, the research team developed the brain implant with IoT connectivity. Even without being physically present in the laboratory, researchers can remotely administer drugs or stimulate specific brain neurons with light in real time via the internet. The device can also be programmed to operate automatically at a preset time. The device is about the size of a sugar cube and is designed not to interfere with the animal's natural behavior. Researchers no longer need to repeatedly approach or handle equipment near the animal, reducing the stress caused by a researcher's presence, which can otherwise affect the animal's behavior and bias experimental results. The implant contains a microfluidic system that precisely delivers drugs to a targeted region of the brain, as well as a micro-LED that enables optical control of specific neurons. Drug delivery and optical stimulation can be controlled independently, or the two functions can be combined. The drug reservoir is designed to be magnetically detachable. Even after the drug is depleted, researchers can replace or refill the reservoir without the need for additional implantation surgery, enabling long-term, repeated experiments. The research team implanted the device in rats and verified its performance over a four-week period. In particular, a researcher in Chicago successfully operated the brain implant in Daejeon, Korea, in real time via the internet, demonstrating that the device can operate reliably over intercontinental distances. The team also conducted an experiment in which cocaine was wirelessly administered to a rat's brain while specific neurons were simultaneously stimulated with light. The results showed that addiction-related behavioral responses could be suppressed, demonstrating the potential of combining drug delivery and optical stimulation for neural circuit research. By eliminating the need for researchers to operate equipment directly beside experimental animals, this technology enables long-term studies of the relationship between brain circuits and behavior under naturalistic conditions. It is expected to be useful for studying conditions that involve long-term changes in neural circuit function and behavior, such as addiction, depression, and neurodegenerative diseases. The technology could ultimately pave the way for intelligent implantable medical devices that combine brain-state sensing with AI to deliver drugs or neural stimulation precisely when needed. < Figure 2. Configuration and operation of the wireless brain implant. >   Professor Jae-Woong Jeong from KAIST said, “This technology transforms wireless brain implants that use light and drugs from short-range control tools into IoT-based brain engineering platforms capable of long-term, automated, and remote experimentation.” He added, “In the long term, it could contribute to the development of intelligent implantable medical devices for the diagnosis and treatment of brain disorders.” Professor Wha Young Kim from Yonsei University said, “This platform allows researchers to remotely and precisely control specific brain circuits over extended periods while animals move freely under naturalistic conditions.” She added, “It is expected to become an important tool for identifying causal relationships between neural circuits and behavior in disease models such as addiction, depression, and neurodegenerative disorders.” Eun Young Jeong, a doctoral student in KAIST's School of Electrical Engineering, and Jong Woo Park, a doctoral student at Yonsei University College of Medicine, served as co-first authors. The study was published on July 29 in the international journal Science Advances. Paper title: IoT-enabled wireless neural implant for chronic, programmable neuropharmacology and optogenetics, DOI: 10.1126/sciadv.aee8648 This research was supported by the Mid-Career Researcher Program and Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT, as well as the Industrial Technology Alchemist Project of the Ministry of Trade, Industry and Energy.

KAIST Uses Light to Distinguish Real from Fake wit..

  < The research team. From left: Dr. Geon Gug Yang and Professor Sang Ouk Kim from KAIST. In the circles, from left: Professor Seok Joon Kwon and Ph.D. student Seong-Gyun Im from Sungkyunkwan University. >   Shine a light, and the real can be distinguished from the fake. KAIST researchers have developed a security technology that uses unique “artificial fingerprints” created by the random assembly of nanoparticles. Although extremely difficult to replicate, these fingerprints can be conveniently authenticated using only a smartphone flashlight and a laser pointer, opening up potential applications in anti-counterfeiting and electronic device authentication. KAIST announced on August 26 that a research team led by Professor Sang Ouk Kim from the Department of Materials Science and Engineering, in collaboration with a team led by Professor Seok Joon Kwon of Sungkyunkwan University, has developed a new foundational security technology based on randomly assembled colloidal nanopatterns—unique microscopic patterns formed by particles too small to be seen with the naked eye. The technology enables authentication using everyday light sources such as smartphone flashlights and laser pointers. < Figure 1. Schematic illustration of polycrystalline colloidal self-assembly and the PUF authentication method. >   Recent advances in artificial intelligence have made cyberattacks increasingly sophisticated, while future quantum computers may pose a threat to conventional cryptographic systems. As a result, growing attention is being paid to technologies that use the unique physical characteristics of products or devices themselves for security, in addition to software-based encryption. A physical unclonable function, or PUF, is a security technology that uses minute physical differences naturally generated during the manufacturing process as security information. Just as every person has a unique fingerprint, microscopic particles form a different arrangement each time they assemble. Even when the same materials and process are used, reproducing the exact positions and orientations of the particles is extremely difficult. The researchers used these differences as “artificial fingerprints” for authenticating products and devices. However, conventional high-security PUFs typically require expensive microscopes, spectroscopic equipment, or imaging systems to read information from their tiny and complex structures, making them difficult to use conveniently in everyday settings. The research team focused on solving this dilemma between high security and easy authentication. By using the self-assembly of spherical particles hundreds of nanometers in size on a water surface, the team created unique structures composed of many small crystalline domains with different sizes and orientations. These structures are different every time they are made, making them difficult to replicate, while also producing clear optical signals when illuminated. The research team implemented an authentication method in which the two patterns generated by each product are registered in advance and subsequently compared with those observed from the actual product. In other words, a single “nanofingerprint” is authenticated using two different light sources: a flashlight and a laser. Much like identifying a person using both their face and fingerprint, verifying one nanostructure in two independent ways strengthens security. When illuminated with ordinary light, such as a smartphone flashlight, the nanostructure produces a unique color and reflection pattern depending on the particle arrangement. When illuminated with a laser pointer, the microscopic particle structure scatters the light in multiple directions, producing a second distinctive optical pattern. To create a counterfeit, a forger would have to reproduce not only the nanoparticle structure itself, but also the exact color and reflection pattern produced under a flashlight and the optical pattern generated under laser illumination—making replication extremely difficult. The researchers also successfully transferred the nanostructures onto a variety of surfaces, including flexible plastics, metals, transparent films, and hydrogels—soft, gel-like materials capable of retaining large amounts of water. The technology could be used to assign a unique “hardware ID” to electronic products and Internet of Things devices for product authentication. It could also serve as an anti-counterfeiting label for luxury goods, artworks, and pharmaceuticals. Because it can be applied to transparent films, it may also be developed into security stickers that do not obscure a product’s design or appearance. < Figure 2. Demonstration of polycrystalline PUF devices for passport, biometric, and underwater authentication. >   Professor Sang Ouk Kim of KAIST’s Department of Materials Science and Engineering said, “The key achievement of this study is that it combines randomly formed structures that are extremely difficult to replicate with a simple authentication method using readily available tools such as a flashlight or laser pointer.” He added, “We expect the technology to develop into a next-generation security solution that can be readily used in everyday applications, including electronic device authentication and anti-counterfeiting labels.” Dr. Geon Gug Yang of KAIST’s Department of Materials Science and Engineering and Ph.D. student Seong-Gyun Im of Sungkyunkwan University’s Department of Chemical Engineering contributed equally as co-first authors. Professors Sang Ouk Kim of KAIST and Seok Joon Kwon of Sungkyunkwan University served as co-corresponding authors. The results were published online on July 23 in the international journal Nature Communications. Paper title: “Dual-space visible light authentication toward high security physical unclonable function” DOI: https://doi.org/10.1038/s41467-026-75781-4 This research was supported by the Mid-Career Researcher Program and the InnoCORE Program funded by the Ministry of Science and ICT, as well as by the Samsung Research Funding & Incubation Center for Future Technology.  

KAIST Identifies a Route to Faster-Charging, Longe..

  < The research team. Clockwise from bottom right: Professor Kang Taek Lee (Department of Mechanical Engineering, KAIST); Professor EunAe Cho (Department of Materials Science and Engineering, KAIST); Yejin Kang, PhD candidate (Department of Mechanical Engineering, KAIST). >     Can electric vehicles charge quickly without sacrificing battery longevity? A KAIST research team has identified a potential solution using a three-dimensional digital twin—a virtual model that recreates the internal microstructure of a real battery electrode. The team found that fast-charging performance and degradation behavior are influenced not only by the amounts of materials and pore space within the electrode, but also by how they are distributed. KAIST (President Choongsik Bae) announced on August 24 that a research team led by Professor Kang Taek Lee from the Department of Mechanical Engineering, in collaboration with Professor EunAe Cho of the Department of Materials Science and Engineering, constructed a 3D digital twin informed by the microstructure and specifications of a commercial graphite anode. Using the model, the researchers quantitatively analyzed localized degradation mechanisms that arise during fast charging. A lithium-ion battery anode consists of graphite, which stores lithium; a binder that holds the graphite particles together; and electrolyte-filled pore space where lithium ions travel. When a battery is charged, lithium ions move into the graphite particles in the anode where they are intercalated and stored. But if charging happens too quickly, some lithium ions cannot enter the graphite in time and instead build up as metallic lithium on the surface—a phenomenon called Li plating. It is similar to cars piling up at the entrance of a parking lot when too many arrive at once and cannot get inside fast enough. If this continues, it can degrade both battery performance and lifespan. During charging, a thin protective film also forms on the graphite surface called the solid electrolyte interphase (SEI) layer. A properly formed SEI layer is necessary, but if it becomes too thick or uneven, it can degrade battery performance. In addition, as lithium enters the graphite particles during charging, the particles expand and push against the surrounding material, creating mechanical stress inside the electrode. These processes occur simultaneously at the microscale, making their individual effects difficult to distinguish experimentally. Existing computational models have also relied mainly on the electrode's average properties, making it hard to capture the complex internal structure and location-dependent behavior within the electrode. To address this, the research team built a "3D digital twin" of the battery electrode based on the structure of an actual commercial graphite anode. The team reconstructed the graphite particles, the binder that holds them together, and the electrolyte-filled pores through which lithium ions travel—all in three dimensions. < Figure 1. Schematic representation of the digital-twin-based anode degradation analysis. >   Using this virtual electrode, the researchers varied the electrode thickness, porosity, and the distribution of the binder, then simulated fast charging to analyze how lithium ions moved. They also examined where Li plating occured, how the protective film formed, and which parts of the anode experienced concentrated stress. The results showed that even when the overall charge capacities were similar, the internal degradation behavior of the anodes could differ significantly depending on how the binder and pore space were arranged inside the electrode. In 50-micrometer (μm) anodes, the difference in charge capacities due to binder distribution was within 4％—meaning there was little apparent difference in charging performance. Inside the electrode, however, the locations where lithium was intercalated and where performance-degrading reactions occurred differed clearly. In particular, when the binder was concentrated near the separator, the available pore space for lithium-ion transport decreased, making it more difficult for lithium ions to move through the anode. It is much like how a narrower road causes traffic congestion. In this case, Li plating near the current collector increased by more than 10％ compared to the anode with an evenly distributed binder. Conversely, when the binder was spread relatively evenly throughout the electrode, lithium-ion transport became more uniform, and the protective film also formed more uniformly. This difference grew larger as the electrode became thicker. In 83 μm-thick anodes, the charge capacity difference between the two binder distributions widened to about 18％. This suggests that making thicker electrodes to store more energy requires carefully designing not just how much material is used, but exactly how it is arranged inside. The location of pore space also affected the stress the electrode experienced. Where there was enough pore space, the surrounding area could accommodate the graphite particles as they expanded during charging. Where pore space was insufficient, the graphite particles had no room to expand, concentrating stress in specific areas. < Figure 2. Electrochemical, side-reaction, and mechanical behavior analyzed in this study. >   Through this study, the research team proposed a new design direction for fast-charging lithium-ion batteries: rather than simply looking at how much binder and pore space an electrode contains, researchers should also consider where and how they are distributed. Using a 3D digital twin makes it possible to examine potential problems inside a battery in virtual space before building and testing multiple electrode designs by hand. The approach is expected to help identify optimal electrode structure, contributing to the development of batteries that can charge faster while maintaining longer service life. "This research is significant in that it used a 3D digital twin to uncover internal battery problems that were difficult to detect from overall charging performance alone," said Professor Lee. He added that properly arranging the binder and pore space inside the electrode could help design batteries that store more energy while charge faster, and last longer. The study, with KAIST PhD candidate Yejin Kang from the Department of Mechanical Engineering as first author, was published in the international journal InfoMat (Impact Factor 19.6) and was for the journal’s back cover on July 7. Paper title: Digital twin quantifies spatial-heterogeneity-driven failure in fast-charging lithium-ion battery anodes, DOI: https://doi.org/10.1002/inf2.70141 This research was supported by the Ministry of Science and ICT's Mid-Career Researcher Support Program, its Convergence Technology Development Program, and the InnoCORE Research Center.    < InfoMat’s back cover image >

KAIST Uses Surface ‘Defects’ to Enhance Droplet Fo..

  < From left: Professor Youngsuk Nam, Ph.D. candidate Jun Soo Kim, Professor Sung Gap Im, and Ph.D. candidate Minjeong Kang. >   A technology that boosts condensation heat transfer performance by up to 5.5 times that of conventional copper surfaces has been developed by helping water droplets form more readily and detach more quickly. It is expected to help improve the energy efficiency of power plants and desalination facilities and enhance the cooling performance of electronic devices. KAIST (President Choongsik Bae) announced on August 23 that a joint research team led by Professor Youngsuk Nam from the Department of Mechanical Engineering and Professor Sung Gap Im from the Department of Chemical and Biomolecular Engineering has developed a technology that controls the thickness and structure of an ultrathin polymer coating applied to a surface, allowing more water droplets to form and the resulting droplets to detach more quickly as water vapor turns into liquid water. Condensation is the process by which water vapor turns into liquid water. It is easy to observe in everyday life, as when droplets form on the surface of a cold beverage cup. In industrial settings, it is widely used to convert steam back into water at power plants, obtain fresh water from seawater, and remove heat generated by electronic devices. During condensation, rapidly removing water from the surface is essential. On ordinary metal surfaces, small droplets merge to form a thin water film. This water film adds thermal resistance, impeding heat flow and reducing heat transfer efficiency, much like layers of winter clothing that slow the loss of body heat. By contrast, when water forms as small droplets and continuously detaches, it repeatedly exposes fresh surface area. This phenomenon, in which water condenses as droplets, is known as dropwise condensation. Put simply, instead of water continuously covering the surface, droplets repeatedly form and fall away. This allows heat to be transferred more effectively. Existing technologies, however, faced a dilemma. Roughening the surface to create more sites where droplets could first form caused the droplets to become caught on the structures and prevented them from detaching easily. Conversely, smoothing the surface helped droplets detach but reduced the number of sites available for new droplets to form. In other words, surface features that promote droplet formation can also make droplets harder to remove, creating a fundamental trade-off between nucleation and droplet mobility. The research team solved this problem by using nanoscale polymer aggregates that had previously been regarded as ‘defects’ in polymer films. The team used initiated chemical vapor deposition (iCVD), a process that deposits gas-phase precursors onto a surface to create an ultrathin polymer film. When the polymer film was made thinner, small polymer aggregates formed densely across the surface and served as nucleation sites where water droplets could readily begin to form. As a result, approximately three times more droplets formed on the thin polymer films than on the thicker films. The team then added a heat treatment step to reduce the force holding droplets to the surface. This allowed droplets to detach easily before growing large. In other words, thinning the polymer film increased the number of sites where droplets could form, while heat treatment helped the resulting droplets detach quickly. The key advance was to control these two competing effects separately: film thickness increased droplet nucleation, while thermal treatment promoted droplet removal. New droplets form again where previous droplets have detached. Much like the next person taking a seat as soon as it becomes vacant, the faster droplets form and detach, the more frequently the surface is renewed, allowing heat to transfer more efficiently during condensation. The research team coated copper tubes commonly used in actual condensers with the polymer film and evaluated their performance. The maximum condensation heat transfer coefficient, a measure of heat transfer ability, reached approximately 88 kW·m⁻²·K⁻¹. This represented heat transfer performance up to approximately 5.5 times higher than that of a conventional copper surface with a water film. The coating also performed more than 50％ better than a conventional hydrophobic coating surface. < Conceptual illustration of the study. A nanoscale polymer film deposited on a copper tube by initiated chemical vapor deposition (iCVD) >   Unlike conventional approaches focused on making surfaces smooth or hydrophobic, this study actively used small surface ‘defects.’ The researchers found that nanoscale particles previously regarded as features to be eliminated could instead help droplets form, and they incorporated that finding into a new surface-design strategy. If applied to power plants or industrial heat exchangers, the technology could help improve energy efficiency by transferring heat more effectively. It is also expected to enable more effective water collection in desalination and water-harvesting devices and faster heat removal for improved cooling of electronic devices. Professor Nam said, “This research is meaningful because it uses nanostructures previously regarded as defects as features that help droplets form. We have presented a new method for improving heat transfer efficiency by separately controlling droplet formation and removal.” He added, “Because this technology can form extremely thin, uniform coatings even on surfaces with complex shapes, we expect it to be used in various energy and environmental applications, including industrial heat exchangers.” Jun Soo Kim, a researcher in the Department of Mechanical Engineering, and Minjeong Kang, a researcher in the Department of Chemical and Biomolecular Engineering, co-authored the study as first authors. The results were published online in the international journal Nature Communications on July 16. Paper title: Rational design of polymer film morphology via structure–performance linkage for enhanced condensation performance DOI: https://doi.org/10.1038/s41467-026-75621-5 This research was supported by the Mid-Career Researcher Program (Ministry of Science and ICT and the National Research Foundation of Korea), the SME Technology Innovation Development Program (Ministry of SMEs and Startups and the Korea Technology and Information Promotion Agency for SMEs), and the Deep-Tech Startup Activation Support Program (Ministry of Science and ICT and Commercialization Promotion Agency for R&D Outcomes, COMPA).

KAIST Develops Core Technology to Reverse Biologic..

  < Top right: Dr. Jongwan Kim (co-first author), Department of Bio and Brain Engineering, KAIST. Group photo (from left in the back row): Dr. Jonghoon Lee (co-author), Dr. Seong-Hoon Jang (co-first author), Corbin Hopper, Ph.D. student (co-author); (front row): Professor Kwang-Hyun Cho. >   Once a cell has locked into an abnormal state — the way cancer cells do — can it ever be restored back to normal? A KAIST research team has identified the ‘molecular lock’ that keeps cells trapped in an altered state, opening a new path toward releasing that lock and reversing a cell’s fate. KAIST (President Choongsik Bae) announced on the 21st of August that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has, for the first time, identified the causal circuits responsible for irreversibility in intracellular molecular networks and developed a fundamental control technology called ROOT that can regulate these circuits and restore biological states to their original condition. Cells in the human body change their state in response to external stimuli. In many cases, however, these state changes are irreversible, in the sense that cells do not return to their original state even after the stimulus disappears. < [Figure 1] The ROOT framework for identifying and controlling the cause of irreversible cell-state transitions >   Irreversibility is essential for maintaining normal biological processes, such as a cell differentiating into one with a specific function. At the same time, it can also drive disease progression — for example, in epithelial–mesenchymal transition, which gives cancer cells the ability to migrate into and invade surrounding tissue. Complicating matters, the circuits that maintain these state changes inside a cell are highly intricate: more than a thousand positive feedback loops are woven throughout the network, in which one molecule activates a series of other molecules that in turn reactivate the original molecule. This is similar to the feedback screech produced when a microphone is placed next to a speaker, where a sound repeatedly amplifies itself. Even a change that starts with an external stimulus can persist after the stimulus is gone, simply because the cell’s own molecules keep reinforcing one another. Until now, it has been extremely difficult to determine which of these countless circuits is actually responsible for locking a cell into an irreversible state. To solve this problem, the team developed ROOT technology, short for Revelation Of the Original circuit of irreversible Transition, which works by representing intracellular regulatory processes as computational logic models and analyzing them through systems biology techniques. Using ROOT, the research team successfully simulated the process in which cells maintain a signal even after an external stimuli is removed, allowing them to identify a set of core circuits that cause irreversibility, which they defined as the “irreversibility kernel.” Going beyond identifying the cause, the team also proposed two groundbreaking control strategies. < [Figure 2] Process of identifying the irreversibility kernel and deriving control strategies using ROOT >   The first, “resetting control,” restores a cell to its state before the change while leaving the cell’s underlying irreversible property intact — comparable to leaving the lock itself in place, but opening the locked door and returning to the starting point. The second, “reversing control,” removes the source of irreversibility itself, allowing a cell to move freely between different states — comparable to disabling the mechanism that automatically locks a door each time it closes, so that afterward the door can be opened and closed again. The team applied the new technique to various biological models, including B-cell differentiation, epithelial–mesenchymal transition in lung cancer, and enterocyte and beta-cell differentiation models based on single-cell transcriptome data, in which the ROOT method accurately identified causal circuits that matched known cell-fate determinants. The team also proposed more effective resetting control strategies, demonstrating that the method can be broadly applied even to models built from real experimental data. Rather than simply removing cells that have become fixed in an abnormal state, as in cancer or aging, the technology is expected to help identify and control the core circuits that keep cells trapped in that state, enabling new treatment strategies that restore cells to a normal condition. Professor Kwang-Hyun Cho said, “The core achievement of this study is identifying the causal circuits behind cells that, once changed, do not return to their original state, and developing a technology to control these circuits and restore cells to their previous condition.” He added, “We expect this technology to be used in developing new treatment strategies that restore abnormally fixed cell states — such as those seen in cancer and aging — back to normal.” < [Figure 3] Analysis of irreversibility in hematopoietic stem cell differentiation using ROOT >   This study was co-led by Dr. Jongwan Kim and Dr. Seong-Hoon Jang of KAIST’s Department of Bio and Brain Engineering as co-first authors, with participation from Dr. Jonghoon Lee and Ph.D. student Corbin Hopper. The research was published on August 13 in Proceedings of the National Academy of Sciences of the United States of America (PNAS), one of the world’s leading scientific journals. Paper title: The structural origin of irreversible transitions in biological networks, DOI: https://doi.org/10.1073/pnas.2600800123   This research was supported by the Mid-Career Researcher Program and the Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.  

KAIST Solves 3D Memory Reliability Problem with ˝O..

  < The research team. Back row, from left: Hyeonjin Lee, Researcher (UNIST); Jimin Kwon, Professor (KAIST); Hyeonho Gu, Researcher (KAIST); Haksoon Jung, Dr. (KAIST); Yongwoo Lee, Dr. (KAIST). Front row, from left: Minho Park, Researcher (UNIST); Youngmin Jo, Dr. (KAIST); Heesoo Yang, Researcher (UNIST); Seunghun Baek, Researcher (UNIST). >   As AI systems become more advanced, memory is required to transfer larger amounts of data at higher speeds. But conventional planar semiconductor scaling is running out of room. A KAIST research team has now addressed a key weakness in three-dimensional, vertically stacked memory devices, opening a new path to faster, more power-efficient AI semiconductors. KAIST (President Choongsik Bae) announced on August 25 that a research team led by Professor Jimin Kwon from the School of Electrical Engineering has developed a new multilayer interlayer dielectric structure that reduces defects and significantly enhances the performance of oxide vertical channel transistors (VCTs), a next-generation memory device. The study was conducted in collaboration with researchers from UNIST, Yonsei University, and other Korean institutions. DRAM, which serves as the main memory in computers, has advanced over the past several decades by scaling down device size while reducing power leakage. More recently, vertical channel structures, in which current flows vertically, have become a key technology for increasing memory density. The challenge is oxygen vacancies — defects caused by the absence of oxygen atoms in the oxide semiconductor — which destabilizes the material's electrical properties. But oxygen cannot simply be supplied without limit: when oxygen is supplied to suppress oxygen vacancies, some of the oxygen tends to migrate further, reaching the metal electrode and oxidizing it, which degrades device performance instead. The channel needed oxygen; the electrode did not. Therefore, selectively controlling oxygen flow became a key challenge. The KAIST team developed a new multilayer interlayer dielectric consisting of silicon nitride/silicon dioxide/silicon nitride (SiN/SiO₂/SiN), engineered to function as an "oxygen tunnel" that steers oxygen selectively toward the channel while blocking its path to the electrode. The structure enabled stable compensation of oxygen vacancies in the oxide semiconductor while simultaneously suppressing unwanted oxidation at the electrode, thereby resolving the trade-off. As a result, the researchers achieved world-class current density and data retention time in oxide vertical channel transistors. < Figure 1. Oxide Vertical Channel Transistor with an Oxygen-Tunnel Structure >   The device also demonstrated outstanding operational stability. Even after more than ten million cycles of harsh electrical stress testing, the threshold voltage shift remained below 50 millivolts (mV), confirming its high reliability as a memory device. The team further evaluated system-level performance by integrating conventional silicon CMOS technology with the new oxide semiconductor platform. The results suggest that this approach could significantly improve the performance of next-generation compute-in-memory (CIM) systems, intelligent semiconductors that perform AI computation directly inside memory. Hyeonho Gu, the first author of the study, said, “This research is significant because it goes beyond improving memory density and addresses the long-standing instability problem in 3D devices through a new approach based on oxygen migration control.” He added, “We expect this technology to play a key role in accelerating the commercialization of ultra-low-power, high-performance compute-in-memory systems required for the AI era.” This study was led by KAIST researcher Hyeonho Gu as the first author and was published on May 20 in Advanced Functional Materials, a leading international journal in materials science. The paper was also selected as a Front Cover article in recognition of its academic significance and originality. < Figure 2. Research Image Selected as the Front Cover of Advanced Functional Materials >   Paper title: Oxygen-Tunnel Indium Tin Oxide Vertical Channel Transistors with Enhanced Current Density and Reliability for Monolithic 3D Compute-In-Memory Systems DOI: https://doi.org/10.1002/adfm.202531989 Author information: Hyeonho Gu (KAIST, first author); Yongwoo Lee (KAIST, corresponding author); Haksoon Jung (KAIST, corresponding author); Jimin Kwon (KAIST, corresponding author); Hoichang Jeong (UNIST, co-author); Yanfeng Zhao (UNIST, co-author); Heesoo Yang (UNIST, co-author); Minho Park (UNIST, co-author); Hyeonjin Lee (UNIST, co-author); Seunghun Baek (UNIST, co-author); Minju Song (UNIST, co-author); Junghwan Kim (UNIST, co-author); Youngmin Jo (KAIST, co-author); Hyunjin Park (Korea Research Institute of Chemical Technology, co-author); Munhyeon Kim (Seoul National University of Science and Technology, co-author); Jae-Joon Kim (Seoul National University, co-author); Kyuho Jason Lee (Yonsei University, co-author); and Byungjo Kim (UNIST, co-author). This research was supported by the National Semiconductor Laboratory Program and the Excellent Young Researcher Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT; the Broadcast and Telecommunications Industry Technology Development Program of the Institute of Information & Communications Technology Planning & Evaluation; and the Super Gap Technology Development Program of the Korea Evaluation Institute of Industrial Technology, funded by the Ministry of Trade, Industry and Energy.

KAIST Develops Semiconductor Neuron That Tunes Noi..

  < Members of the research team. From left: Dae Hee Kim (Ph.D. candidate), Tae Wook Ko (Ph.D. candidate), Dr. Do Hoon Kim (first author), Seo Eun Jang (Master >   In electronic devices, irregular fluctuations in signals are generally referred to as “noise.” Because noise interferes with accurate information processing, conventional semiconductor technology has mainly treated it as something to be reduced or eliminated. However, neurons in the human brain do not respond in exactly the same way every time, even to the same stimulus. Tiny internal variations in neurons change when and how often neurons are fired, and this probabilistic operation is one of the brain’s key information-processing features. Inspired by this, KAIST researchers have developed a next-generation semiconductor technology that does not remove current noise generated in memristors, but instead tunes it to a desired level and uses it to process different types of signals. KAIST (President Choongsik Bae) announced on the August 16 that a research team led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a new neuromorphic neuron technology that uses noise generated in semiconductor devices for information processing, enabling selective encoding of time-series signals across different frequency bands. ※ Neuromorphic technology: A technology that processes information by mimicking the way the human brain and neurons operate. In general, noise generated in semiconductors is regarded as an obstacle to accurate signal processing. For this reason, most electronic devices are designed to reduce or eliminate noise as much as possible. The human brain, however, works differently. Neurons, the nerve cells of the brain, do not always respond in the same way to the same stimulus because of internal probabilistic fluctuations. This irregularity actually helps the brain flexibly respond to a wide range of situations and sensory signals. < Figure 1. Comparison of biological neurons and memristor-based probabilistic neurons >   The research team used a memristor in this study. A memristor is a semiconductor device that changes its resistance state in response to electrical stimulation and remembers that state. Until now, current noise generated in memristors has mainly been used for random number generation, which creates unpredictable numbers, or for probabilistic computing. However, previous studies have largely focused on using the inherent randomness of memristors as it is. Technologies that can tune probabilistic response characteristics according to need had not been sufficiently realized. < Figure 2. Memristor noise and spike-generation characteristics dependent on the resistance state >   The key insight of this study is that when the resistance state of a memristor is changed, the magnitude and behavior of its current noise also change. By presetting the resistance state of the memristor, the probability of spike generation and the response range can vary even under the same input. Using this principle, the research team implemented a “programmable probabilistic neuron (PPN)” that treats noise not simply as instability, but as an information-processing resource that can be tuned in a desired way. This neuron can be configured to respond differently depending on how rapidly an input signal changes, in other words, its frequency. By changing only the resistance state of the memristor, the same circuit can be switched to respond sensitively to slow human activity signals in the hertz (Hz) range or fast speech signals in the kilohertz (kHz) range. Hz and kHz are units that indicate how many times a signal repeats per second, with 1 kHz equal to 1,000 Hz. < Figure 3. Reconfigurable time-series encoding for human activity and speech signals >   In simple terms, a single artificial neuron can be reconfigured according to the speed of the signal it needs to process. When processing slowly changing signals such as human movement, it can operate in a way suited to slow variations; when processing rapidly changing signals such as speech, it can be adjusted to capture short and fast changes effectively. The research team verified the technology using signals with different frequency ranges. The system encoded and classified human activity signals in the Hz range and speech signals in the kHz range, achieving accuracies of 94.8％ in human activity recognition and 95.0％ in speech recognition. Professor Kyung Min Kim said, “The significance of this study lies in demonstrating that memristor noise can be harnessed as a tunable information-processing resource, rather than simply treated as an error or instability,” adding, “Because the same hardware can be reconfigured for signals of different speeds and frequencies, it could be used as a signal-processing technology for future low-power edge neuromorphic systems.” This study was led by Dr. Do Hoon Kim from the Department of Materials Science and Engineering as first author, and was published in the internationally renowned materials science journal Advanced Materials on August 05.   Paper title: Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding, DOI: https://doi.org/10.1002/adma.74529   This research was supported by the Basic Research Program in Science and Engineering and the PIM Artificial Intelligence Semiconductor Core Technology Development Program of the Ministry of Science and ICT and the National Research Foundation of Korea.

Patient-Specific ˝Blood–Brain Tumor Barrier Chip˝ ..

  < Members of the research team. Top row, from left: Gaeun Lee, Ph.D. candidate at Sungkyunkwan University; Professor Jungho Ahn of Sungkyunkwan University; Professor Jaejoon Lim of Bundang CHA Medical Center; and Professor Youn-Jung Kang of CHA University. Bottom front row: Minsu Ryoo, Ph.D. candidate at KAIST, and Professor Song Ih Ahn of KAIST. Bottom back row: Nayeong Kang, master’s student at KAIST, and Jinwoo Jung, Ph.D. candidate at KAIST. >   Even for the same brain tumor treated with the same anticancer drug, the effect can differ from patient to patient. A Korean research team has developed a chip that recreates a patient's own tumor cells together with the surrounding peritumoral vascular environment, making it possible to predict patient-specific treatment responses in advance. KAIST (President Choongsik Bae) announced on August 18 that a research team led by Professor Song Ih Ahn from the Department of Mechanical Engineering, in collaboration with Professor Jungho Ahn's team at Sungkyunkwan University, Professor Jaejoon Lim of CHA Bundang Medical Center, and Professor Youn-Jung Kang's team at CHA University, has developed a patient-specific blood–brain tumor barrier (BBTB) chip capable of predicting treatment responses in glioblastoma patients. Glioblastoma is one of the most lethal malignant brain tumors, hard to treat because the cancer cells spread rapidly into normal brain tissue and tumor characteristics differ from patient to patient. The brain is also protected by a "blood–brain barrier," which blocks harmful substances in the blood from entering brain tissue. The problem is that it blocks anticancer drugs too, so not enough of the drug reaches the tumor. When glioblastoma develops, this vascular barrier changes as well. How much it changes differs from patient to patient, which is one reason the same drug can work differently in different people. Predictions of treatment response have so far relied mainly on tumor genetic information and biomarkers, which cannot capture the patient-specific vascular environment around the tumor or the resulting drug response. To address this, the team built a chip that includes not only the patient's tumor cells but also the vascular barrier. This barrier is the "route" an anticancer drug must travel to reach the tumor. Patient-derived glioblastoma cells were co-cultured with brain vascular endothelial cells and astrocytes inside a small microfluidic chip. Together they recreate the boundary where tumor tissue meets normal brain tissue. The design also accommodates perivascular and immune cells, allowing the tumor's vascular environment to be reproduced more precisely. < Figure 1. The upper-left schematic shows the structure of a microfluidic chip designed to recreate the glioblastoma margin. >   Using tumor cells from three glioblastoma patients, the team built patient-mimicking BBTB chips and applied temozolomide (TMZ) and bevacizumab (BEV), the standard agents in glioblastoma treatment, to compare responses. The three patients showed the same result on conventional genetic testing, the MGMT promoter methylation biomarker, and were therefore expected to respond similarly. On the chip, however, both the vascular barrier characteristics and the drug responses differed from one patient to another. The chip results showed a high level of agreement with the patients' actual clinical courses, indicating that outcomes can vary with the state of the vascular barrier even when genetic information is similar. The core advance is that the platform evaluates not only whether cancer cells respond to a drug, but how a treatment acts within a given patient's tumor environment. If predictive performance and reproducibility are validated in a larger cohort, several agents could be tested on a chip made from a patient's own tumor cells to select the most promising strategy in advance. The same environment could also serve to screen new drug candidates. Professor Song Ih Ahn said, "This study is meaningful in that it presents a platform that recreates patient-derived tumor cells together with the blood–brain tumor barrier, allowing patient-to-patient differences in treatment response to be evaluated in a way that closely reflects reality," adding, "We hope to validate it in a larger patient population and develop it into a preclinical evaluation platform for establishing personalized treatment strategies and for new drug development." < Figure 2. Conceptual illustration of the research (AI-generated image) >   Minsu Ryoo, a doctoral student in KAIST's Department of Mechanical Engineering, and Gaeun Lee, a doctoral student at Sungkyunkwan University, participated as co-first authors. The results were published on June 27, 2026, in Small, an international journal in materials and nanoscience, and were selected as the journal's Front Cover.   Paper title: Human Blood-Brain Tumor Barrier on a Chip to Investigate Personalized Treatment for Glioblastoma Patients DOI: 10.1002/smll.202506712   This research was supported by the Korea–US Collaborative Research Fund of the Ministry of Health and Welfare and the Korea Health Industry Development Institute (RS-2024-00468873), and by the Early-Career Researcher Program of the Ministry of Science and ICT and the National Research Foundation of Korea (RS-2026-25487930).