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KAIST Solves 3D Memory Reliability Problem with "Oxygen Tunnel" Structure, Boosting AI Chip Performance and Reducing Power Consumption
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.
The device also demonstrated outstanding operational stability. Even after more than ten million cycles of harsh electrical stress testing, the threshold voltage shift remained below 50 millivolts (mV), confirming its high reliability as a memory device.
The team further evaluated system-level performance by integrating conventional silicon CMOS technology with the new oxide semiconductor platform. The results suggest that this approach could significantly improve the performance of next-generation compute-in-memory (CIM) systems, intelligent semiconductors that perform AI computation directly inside memory.
Hyeonho Gu, the first author of the study, said, “This research is significant because it goes beyond improving memory density and addresses the long-standing instability problem in 3D devices through a new approach based on oxygen migration control.” He added, “We expect this technology to play a key role in accelerating the commercialization of ultra-low-power, high-performance compute-in-memory systems required for the AI era.”
This study was led by KAIST researcher Hyeonho Gu as the first author and was published on May 20 in Advanced Functional Materials, a leading international journal in materials science. The paper was also selected as a Front Cover article in recognition of its academic significance and originality.
Paper title: Oxygen-Tunnel Indium Tin Oxide Vertical Channel Transistors with Enhanced Current Density and Reliability for Monolithic 3D Compute-In-Memory Systems
DOI: https://doi.org/10.1002/adfm.202531989
Author information: Hyeonho Gu (KAIST, first author); Yongwoo Lee (KAIST, corresponding author); Haksoon Jung (KAIST, corresponding author); Jimin Kwon (KAIST, corresponding author); Hoichang Jeong (UNIST, co-author); Yanfeng Zhao (UNIST, co-author); Heesoo Yang (UNIST, co-author); Minho Park (UNIST, co-author); Hyeonjin Lee (UNIST, co-author); Seunghun Baek (UNIST, co-author); Minju Song (UNIST, co-author); Junghwan Kim (UNIST, co-author); Youngmin Jo (KAIST, co-author); Hyunjin Park (Korea Research Institute of Chemical Technology, co-author); Munhyeon Kim (Seoul National University of Science and Technology, co-author); Jae-Joon Kim (Seoul National University, co-author); Kyuho Jason Lee (Yonsei University, co-author); and Byungjo Kim (UNIST, co-author).
This research was supported by the National Semiconductor Laboratory Program and the Excellent Young Researcher Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT; the Broadcast and Telecommunications Industry Technology Development Program of the Institute of Information & Communications Technology Planning & Evaluation; and the Super Gap Technology Development Program of the Korea Evaluation Institute of Industrial Technology, funded by the Ministry of Trade, Industry and Energy.
2026.08.20 View 2548 -
KAIST Turns Once-Troublesome Protons into an Energy Storage Resource for Batteries
Batteries that use water-based electrolytes have a relatively low risk of fire and are inexpensive, but they have faced limitations in storing large amounts of energy. KAIST researchers have succeeded in using a reaction previously regarded as a “troublemaker” that degrades battery performance to store more energy instead. This achievement opens up the possibility of next-generation water-based batteries capable of storing more energy while charging and discharging rapidly.
KAIST (President Choongsik Bae) announced on the 19th that a research team led by Sarah S. Park from the Department of Chemistry has developed a new electrode material that sequentially stores zinc ions (Zn²⁺) and protons. The material is based on a two-dimensional conductive metal–organic framework (MOF), a structure in which metals and organic molecules are connected to form microscopic pores.
Aqueous zinc-ion batteries use water-based electrolytes. An electrolyte is a substance that allows ions to move inside a battery. Because aqueous zinc-ion batteries have a relatively low risk of fire, are inexpensive, and impose a low environmental burden, they are attracting attention as a next-generation option for large-scale energy storage systems (ESS).
Because zinc ions carry electrical charge, are divalent ions and slow movement inside electrodes, making it difficult to achieve both high capacity and fast charge–discharge performance at the same time. Protons, by contrast, are extremely small and can move very quickly, making them an ideal charge carrier. However, when too many protons enter an electrode, byproducts form on the electrode surface, interfering with the movement of zinc ions and lowering battery performance. For this reason, protons have traditionally been regarded as “troublemakers” that degrade battery performance, and research has focused on suppressing their reactions.
The research team took a different approach. Instead of suppressing proton reactions, the research team precisely controlled the order in which zinc ions and protons are stored so that both could be used for energy storage.
To accomplish this, they introduced amine functional groups into the micropores of a two-dimensional conductive MOF, designing the material so that it would react with protons only when a certain voltage was reached. By designing the amine functional groups to store protons only at a specific voltage, the researchers enabled zinc ions to be stored first, followed by the additional storage of protons.
The principle is similar to first placing large pebbles in an empty bottle and then filling the gaps between them with fine sand. The electrode is used more efficiently by storing the relatively large zinc ions first and then adding the smaller protons.
In practice, zinc ions were stored first in the higher-voltage range, while protons were additionally stored as the voltage decreased. In the Cu₃(HHTATP)₂ electrode material developed by the research team, zinc ions and protons participated in energy storage sequentially in different voltage ranges.
The sequence is particularly important. If protons react too early, by-products may form and obstruct the movement of zinc ions. By enabling protons to react only after the zinc ions had been stored, the researchers reduced interference between the two storage processes.
The newly developed electrode material achieved a high storage capacity of 368.7 mAh g⁻¹ at 0.5 A g⁻¹. In simple terms, this means that a small amount of electrode material can store a large amount of electricity.
Even when the charging and discharging rate was increased sixteenfold, the electrode retained 46.9% of its initial storage capacity—nearly half. Batteries generally store less energy as their charging and discharging rates increase, but the new electrode maintained substantial storage capacity even under rapid charging and discharging conditions. It also demonstrated stable performance after more than 500 rapid charge–discharge cycles.
Using various X-ray analysis techniques, the research team confirmed that zinc ions were stored first, followed by additional proton storage. The researchers also found that the process of storing and subsequently releasing protons could be repeated.
This study is significant because it overcomes the difficulty of simultaneously achieving high capacity and rapid ion transport in porous electrode materials. In particular, it presents a new direction for developing next-generation aqueous zinc-ion batteries by demonstrating that the storage sequence of different ions can be controlled through molecular-level design.
The findings present a new design approach that could enable safe and economical aqueous zinc-ion batteries to store more energy while charging and discharging rapidly. The approach is expected to help improve the performance of water-based batteries used in applications such as large-scale energy storage systems.
Professor Sarah S. Park from KAIST said, “This study demonstrates that protons, previously regarded as ‘troublemakers’ that could degrade battery performance, can instead be used to store more energy. We expect that applying this principle to various electrode materials will lead to the development of batteries capable of rapidly storing larger amounts of energy.”
POSTECH doctoral student Geunchan Park and master student Gyuwon Lee, participated as co-first authors. The study was published in the international chemistry journal Chem on July 7.
Paper title: Sequential Zn²⁺–H⁺ Storage in a 2D Conductive Metal–Organic Framework for Advanced Aqueous Zinc-Ion Battery
DOI: 10.1016/j.chempr.2026.103129
Author information: Geunchan Park (co-first author), Gyuwon Lee (co-first author), Kangmin Kim (second author), Sarah S. Park (corresponding author)
This research was supported by the Basic Research Program of the National Research Foundation of Korea and the National Supercomputing Center.
2026.08.19 View 3453 -
Patient-Specific "Blood–Brain Tumor Barrier Chip" Predicts How Glioblastoma Patients Will Respond to Treatment
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.
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."
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).
2026.08.18 View 3103 -
KAIST Develops Semiconductor Neuron That Tunes Noise to Selectively Process Signals
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.
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.
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.
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.
2026.08.18 View 3259 -
KAIST Controls the Rotation Direction of Light Without Complex New Materials
A new pathway has opened for controlling the rotation direction of light simply by changing how molecules are arranged, without having to synthesize complex new materials. Circularly polarized light is a special form of light that travels while rotating like a pinwheel either to the left or to the right. Because different rotation directions can carry different information, it is drawing attention as a key light source for next-generation displays, optical communications, and security technologies. KAIST researchers have developed a platform technology that arranges symmetric molecules into “microscopic pinwheels,” enabling circularly polarized light with a desired rotation direction.
KAIST (President Choongsik Bae) announced on August 14 that a research team led by Professor Dong Ki Yoon from the Department of Chemistry, in collaboration with researchers from Chungnam National University, Ajou University, Yonsei University, and Japan’s RIKEN, has developed a technology that spatially confines symmetric non-chiral liquid crystal molecules and applies an electric field to form micrometer-scale chiral pinwheel structures, then permanently replicates them onto polymer nanofibers.
Chirality refers to the property of an object whose mirror image cannot be perfectly superimposed on the original, like a person’s left and right hands. Chirality is a key property not only of biological molecules such as proteins and DNA, but also of optical materials used in next-generation displays, optical sensors, and optical communications.
Until now, producing chiral optical materials has generally required the complex synthesis of molecules with asymmetric structures or the addition of large amounts of chiral substances. This has made fabrication complicated, limited the range of usable materials, and made it difficult to realize chiral structures with the same handedness over a large area.
To address this challenge, the research team proposed a new approach based on the idea that “structure creates function.”
The team applied to molecules the same principle by which the same sheet of paper can form either a clockwise or counterclockwise pinwheel depending on how it is folded. They focused on the fact that even symmetric molecules can form structures with different handedness depending on how they assemble.
The researchers first induced rod-shaped molecules to self-assemble into microscopic pinwheel-like structures. They then added an extremely small amount of chiral additive, less than 1% of the total material, to guide all of the pinwheels to face the same direction. The team then successfully replicated this structure onto polymer nanofibers.
When a conventional luminescent material was coated onto this structure, circularly polarized light rotating in opposite directions was emitted depending not on the luminescent material itself, but on the direction of the pinwheel structure. Circularly polarized light is a special form of light that rotates to the left or right as it travels, and because each rotation direction can carry different information, it can be used in next-generation displays, optical communications, and anti-counterfeiting technologies.
In other words, the study showed that the properties of light can be controlled simply by changing the structure on which a light-emitting material is placed, rather than by changing the light-emitting material itself. Put simply, just as the same LEGO blocks can form completely different shapes depending on how they are assembled, the same molecules can produce different optical properties depending only on how they are arranged.
Professor Dong Ki Yoon said, “The key point of this study is that we controlled the rotation direction of light not through the complex chemical structure of chiral molecules, but only through the way molecules are arranged,” adding, “This work presents a new optical material design principle that can be applied to next-generation displays, AR and VR optical devices, polarization sensors, and optical communications without the need to develop complex new materials.”
Jeong Yeon Han, the first author and a Ph.D. candidate, explained, “In conventional approaches, left-handed and right-handed structures tended to form together, canceling out chiral properties. In this study, however, we succeeded in aligning the structures in a single direction over a large area by designing an extremely small amount of additive to select only one rotation direction.”
This study was led by Ph.D. candidate Jeong Yeon Han as the first author, and the research results were published in the international journal Nature Communications on August 05.
Paper title: Microchiral pinwheel arrays based on achiral molecules,
DOI: 10.1038/s41467-026-76089-z
Authors: Jeong Yeon Han (first author), Won Kyung Park, Byeongil Noh, Fumito Araoka, Sungwook Jung, Byeong Hak Jhun, Youngmin You, Yoonsu Park, Kyung Jin Lee*, Jung-Moo Heo*, and Dong Ki Yoon* (*corresponding authors)
This research was supported by the Technology Innovation Program of the Ministry of Trade, Industry and Energy, the InnoCORE Program of the Ministry of Science and ICT, and the National Research Foundation of Korea.
2026.08.14 View 3638 -
KAIST Develops High-Efficiency, Eco-Friendly Hydrogen Separation Membrane That Filters Hydrogen Through a Molecular “Network”
For hydrogen to be widely used as a clean energy source in everyday life, technologies that can extract only hydrogen with high purity from mixed gases are essential. KAIST researchers have presented a new strategy for developing high-performance separation membranes that can selectively filter hydrogen for clean hydrogen energy production.
KAIST (President Choongsik Bae) announced on the 13th of August that a research team led by Professor Tae-Hyun Bae of the Department of Chemical and Biomolecular Engineering has successfully introduced hydrogen-selective transport pathways at the angstrom scale inside polymer membranes and clarified their separation performance through the concept of “network completeness.”
*Angstrom (Å): An extremely small unit of length used to measure wavelengths of light or the size of atoms and molecules. One angstrom is one hundred-millionth of a centimeter, or one ten-billionth of a meter, roughly one-millionth the thickness of a human hair.
Hydrogen is drawing attention as an eco-friendly energy source because it does not emit pollutants when used. However, separating hydrogen with high purity from mixed gases generated during production remains a key challenge for commercialization.
Crystalline porous materials such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are advantageous because their pores can be designed uniformly. However, they are difficult to fabricate over large areas without defects and have limitations in separating small molecules such as hydrogen. Polymer membranes, by contrast, are easier to process and scale up to large areas, but because their pore formation is difficult to control precisely, it has been challenging to raise their separation performance beyond a certain level.
To combine the advantages of both types of materials, the research team designed a modular network structure in which polymer chains are linked by crosslinkers. In this process, the team focused on the limitation that conventional indicators such as the degree of crosslinking (CD) and effective crosslinking degree (ECD), which have been used to describe the extent of crosslinking, cannot determine whether pores useful for separation have actually been formed.
The researchers therefore proposed a new metric called the Bridge Connectivity Degree (BCD), which refers to the proportion of crosslinkers that are connected at both ends to form complete pathways. This made it possible to quantitatively apply the concept of “complete framework connectivity,” which has been emphasized in inorganic porous materials, to polymer networks as well.
The newly developed membrane, ms-oDMB-DB50, achieved a high bridge connectivity degree of 73%, and both its hydrogen permeability and hydrogen/nitrogen selectivity improved significantly compared with the original material, DB50. Analysis showed that the membrane contains numerous ultramicropores smaller than 3 Å, which carbon dioxide cannot access. The research team also proposed a “density-probe method,” using helium molecules, which are smaller than hydrogen, as probes to experimentally verify the existence of these ultramicropores.
The newly developed membrane also operated stably for 100 hours without any loss of performance. Its tensile strength, the force the membrane can withstand without breaking, was about twice that of previously reported high-performance polymer membranes, confirming that it also has the robust durability needed for industrial processes.
Dr. Hongju Lee said, “There have been previous attempts to combine the advantages of these two types of materials, but this study is different in that it defines ‘how completely the network is connected’ as a quantitative value and directly links that value to separation performance,” adding, “We hope this study will serve as a starting point for extending reticular synthesis, a design principle used for inorganic molecular sieves, to polymer membranes.”
Professor Tae-Hyun Bae said, “By stitching polymer chains together with crosslinkers that fit together like Lego blocks, we formed a network inside the membrane that selectively allows only small hydrogen gas molecules to pass through.”
This paper was led by Dr. Hongju Lee, currently a postdoctoral researcher at the Korea Institute of Science and Technology, as first author, with Professor Tae-Hyun Bae as corresponding author. The research was published on July 23 in the international journal Nature Communications.
Paper title: Network completeness enables angstrom-scale transport pathways in polymer membranes,
DOI: https://doi.org/10.1038/s41467-026-73860-
Author information: Hongju Lee, formerly of KAIST and currently at the Korea Institute of Science and Technology, first author; Suhyeon Choi, KAIST, second author; and Tae-Hyun Bae, KAIST, corresponding author
This research was supported by the 2025 Global C.L.E.A.N. Project and the Mid-Career Researcher Program under the Basic Research Program, funded by the Ministry of Science and ICT.
2026.08.13 View 2733 -
KAIST Develops AI to Detect ‘Foreign-Linked Opinion Manipulation’ in 110 Million News Comments
During election seasons or major national issues, online news comment sections often become heated spaces of conflict across gender, generation, and political lines. For years, there have been persistent concerns that behind some of these conflicts may lie “foreign winds,” or interventions by foreign actors seeking to manipulate public opinion and deepen social divisions. A KAIST research team has now developed a technology that uses big data on two decades worth of news comments and artificial intelligence (AI) to precisely detect traces of such hidden influence operations.
KAIST (President Choongsik Bae) announced on the 12th of August that a joint research team led by Professor Wonjae Lee of the Graduate School of Culture Technology, Professor Meeyoung Cha of the School of Computing (Scientific director at Max Planck Institute for Security and Privacy) and Professor Alice Oh of the School of Computing, in collaboration with Professor Thorsten Holz of the Max Planck Institute, has developed an explainable AI technology that automatically detects patterns suspected of foreign-linked influence operations in online news comments and provides specific evidence for its judgments.
The organized and repeated posting of comments or content by certain actors to shape public opinion in a desired direction is known as an “online influence operation.” Existing AI-based detection technologies have had a key limitation: even when they classify certain accounts as belonging to a “blacklist,” they often fail to provide clear evidence explaining why those accounts should be considered influence-operation accounts.
To overcome this limitation, the research team used 70 foreign-linked accounts previously identified by the Institute for National Security Strategy as starting points. They then tracked groups of accounts connected to them or repeatedly commenting on the same news articles, ultimately collecting and analyzing a large-scale dataset of 110 million comments posted on Naver News over a 20-year period from 2006 to 2025.
In particular, the AI developed by the research team examines accounts through a careful three-step process. First, it checks whether there are clues suggesting that the author may be linked to a foreign source. Second, it examines whether the comment contains emotionally polarizing expressions, such as moral condemnation or blind praise. Third, it identifies which country or target the emotional framing is directed toward.
The model does not stop at simply labeling an account as suspicious. It also highlights the specific phrases in the comments that served as the basis for its judgment. The team further combined this with multidimensional behavioral-pattern analysis, including account activity frequency, account lifespan, and activity links with other suspected accounts. As a result, among approximately 4 million Naver News users, the model ultimately identified 23,998 accounts exhibiting patterns consistent with suspected public-opinion manipulation.
The analysis also revealed the more subtle strategy of these suspected accounts. Their main target was not the victory of a particular political camp, but rather the maximization of division and confrontation within Korean society.
Among the top 10 targets that drew the highest public engagement, measured through likes and other reactions, seven were prominent domestic political figures. Notably, the attacks were not concentrated on a single party or ideology. Former and current presidents, presidential candidates, and political parties from both progressive and conservative camps were targeted across the spectrum. According to the research team, this suggests a sophisticated strategy aimed not so much at supporting a particular group, but at inflaming domestic political conflict and increasing social distrust and polarization.
This study is significant because it provides data-based evidence for influence-operation activity that had previously been discussed largely in terms of suspicion, while also offering a potential defense mechanism for protecting healthy online public discourse. In the future, portal platforms and related organizations could use this technology during elections or national crises to monitor the influx of suspicious accounts in real time and prioritize the review of coordinated attacks against domestic political figures. However, the research team emphasized that the AI should not be used to block accounts indiscriminately, but rather as an explainable content-moderation tool that supports the judgment of expert reviewers.
Professor Wonjae Lee said, “By analyzing 20 years of data, we found that suspected accounts tended to use messages criticizing Korea and domestic political figures rather than directly praising foreign countries, and that these messages gained higher visibility,” adding, “This research can provide empirical criteria for when and which messages platforms and monitoring organizations should prioritize for review, especially during socially sensitive periods such as elections.”
Professor Alice Oh said, “This is a meaningful achievement in which AI precisely identified not only the surface meaning of words in massive comment datasets, but also subtle emotional patterns and organized behavioral signals intended to provoke conflict,” adding, “It can become a powerful defense system against online influence operations, which are becoming increasingly sophisticated.”
Professor Meeyoung Cha said, “This study goes beyond simple blacklist-account analysis and represents the outcome of actionable data science that addresses real-world problems and drives practical change,” adding, “In an online environment where social conflict is deepening, we hope this technology will serve as a practical tool for protecting the transparency and trustworthiness of the digital public sphere.”
This research was led by KAIST Ph.D. candidate Jaehong Kim and master’s student Hyeonseung Kim as co-first authors. The paper is scheduled to be presented at the USENIX Security Symposium 2026, one of the most prestigious conferences in the field of computer security.
Paper title: Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea,
DOI: 10.48550/arXiv.2606.22785
This research was supported by the Hyundai Motor Chung Mong-Koo Foundation, the Institute of Information & Communications Technology Planning & Evaluation, and the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2026.08.12 View 4313 -
KAIST Shapes a " Templates a ‘Gas Lattice’ in Porous Materials”: The Moment Gas Forms a Crystal-like Lattice
Capturing carbon or storing hydrogen to combat global warming requires compressing gases into sponge-like porous materials. Until now, gas molecules were thought to adsorb in a disordered manner throughout the pores. But what if invisible gas molecules could be lined up in regular order — like ice crystals or LEGO bricks?
KAIST (President Choongsik Bae) announced on August 11 that a research team led by Professor Jihan Kim of the Department of Chemical and Biomolecular Engineering has developed a computational framework that combines large-scale screening of metal–organic frameworks (MOFs)* with machine-learning-guided inverse design. Focusing on the “gas lattice”—a crystal-like ordered state formed by gas molecules under confinement—the framework enables researchers to explore a vast range of MOF structures and design candidate porous materials capable of stabilizing desired gas arrangements.
*Metal–organic framework (MOF): a material built from metal ions or clusters connected by organic linkers to create countless microscopic pores; MOFs are promising eco-friendly materials used to store or separate gases.
Using xenon (Xe), a monatomic noble gas, as a model system, the research team identified a specific cobalt-based porous material — Co-CAU-36 — that stabilizes xenon in a regular lattice. Computer simulations (GCMC) confirmed that xenon inside this material does not spread out randomly, but instead lines up in a body-centered cubic (BCC) lattice, a well-defined, crystal-like arrangement. This is a breakthrough because gas crystallization was achieved within the pores without the extreme bulk pressures normally required by using the pore structure as a ‘template’.
Striking results also emerged when the team examined the separation of xenon (Xe) and krypton (Kr), a gas mixture of industrial importance. Inside the framework, xenon preferentially occupies an ordered shell region, displacing krypton toward the pore core — a separation behavior that had not been reported before.
To show that the phenomenon could be deliberately designed rather than occurring incidentally, the researchers combined machine learning with a genetic algorithm and used inverse design to identify candidate porous structures targeting BCC- and FCC-like lattices.
The findings may have applications in advanced energy and environmental technologies that depend on precise control of molecular arrangement, including carbon capture and separation, selective catalytic reactions, and gas storage.
"This research is the first demonstration of a gas forming a crystal-like ordered state inside a porous material," said Professor Jihan Kim. He added that the work's significance lies in moving beyond conventional approaches focused primarily on increasing adsorption capacity, toward treating the arrangement of gas molecules itself as a design target.
"If this approach can be extended to more complex molecules, such as carbon dioxide or water, it could become an important starting point for designing tailored materials for gas separation and storage," Professor Kim added.
Younghun Kim and Dohoon Kim, PhD candidates in KAIST's Department of Chemical and Biomolecular Engineering, are co-first authors, with Seungwoo Kim, a master's candidate, and Yunsung Lim, a PhD, serving as co-authors. The findings were published online on June 23 in the international academic journal Nature Communications.
Paper title: Framework-templated gas lattices in metal-organic frameworks
DOI: 10.1038/s41467-026-74776-5This work was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (Project Numbers RS-2024-00451160 and RS-2024-00435493).
2026.08.11 View 2228 -
KAIST Develops ‘Chameleon AI Semiconductor’ with Programmable Response Speeds
AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data changing at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots, and wearable devices.
KAIST (President Choongsik Bae) announced on August 7 that a research team led by Chair Professor Shinhyun Choi from the School of Electrical Engineering and the Graduate School of Semiconductor Technology has developed a programmable dynamic memtransistor (PDM), a semiconductor device whose time-response characteristics can be adjusted to multiple states and retained, as well as an integrated array based on the device.
A memtransistor is a next-generation semiconductor device that combines the information-storage function of memory with the computing function of a transistor. In the developed PDM, the ability to process data while retaining previous information allows its response characteristics to be adjusted and retained for incoming data.
Today’s computers and smartphones require complex software processing to analyze data that changes over time, resulting in large computational loads and high power consumption. To address this, researchers have been studying technologies that allow semiconductor hardware itself to process data directly. However, conventional devices have had fixed response speeds that cannot be changed once the device is fabricated.
The research team overcame this limitation by introducing a dual-layer structure inside the transistor, combining a charge storage layer that accumulates and processes data with an electron trapping layer that controls the response speed in a nonvolatile manner.
In the PDM developed by the research team, incoming data is processed in the charge storage layer, while the electron trapping layer controls, across multiple levels, the recovery speed at which the semiconductor returns to its original state. In experiments, the team succeeded in tuning the current recovery time over an approximately 5-fold range and the characteristic frequency over a range of more than 10-fold.
In particular, in experiments involving the prediction of data in which fast and slow changes are intricately mixed, the PDM reduced prediction errors by as much as 40 times compared with conventional fixed-response semiconductor devices. The PDM enables accurate information processing even when handwriting or object-movement speeds vary, by using response characteristics configured to match different input timescales. Once the response characteristics are set, the device remembers them without requiring a continuous external power supply, and it does not require complex preprocessing of input data. Because it is fully compatible with materials used in widely adopted commercial semiconductor processes, it is also highly advantageous for mass production and commercialization.
The research team fabricated a PDM array and used it to predict complex data, confirming that it achieved accuracy comparable to conventional software-based systems while consuming far less energy.
“This study demonstrates an AI semiconductor whose response characteristics can be programmed to efficiently process data changing at different speeds,” said Chair Professor Choi. “We expect it to become a core technology that improves the performance of AI devices such as autonomous vehicles, robots, and wearables while reducing their power consumption.”
This research was led by KAIST Graduate School of Semiconductor Technology Ph.D. candidate Dae-won Kim as the first author, with Yoonho Cho, Seokho Seo, Yujin Kim, See-On Park, Taehwan Jang, and Chaebin Park participating as co-authors. Young Taek Oh and Fellow Jae-Duk Lee of Samsung Electronics’ Semiconductor R&D Center also participated as co-authors, and Chair Professor Shinhyun Choi served as the corresponding author. The research was published in July in the internationally renowned journal Nature Communications on July 4.
Paper title: Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing,
DOI: https://doi.org/10.1038/s41467-026-75211-5
This research was supported by the National R&D Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT, the ETRI R&D Support Program of the Institute of Information & Communications Technology Planning & Evaluation, the HRD Program for Industrial Innovation of the Korea Institute for Advancement of Technology funded by the Ministry of Trade, Industry and Energy, Samsung Electronics, and others.
2026.08.07 View 4239 -
KAIST Reconstructs Transparent Structures Through Dynamic Scattering Layers in a Single Shot
A KAIST research team has developed a technology that reconstructs the shape, optical thickness, and position of a transparent object hidden between two dynamic scattering layers from a single shot. The technology could enable precision inspection of transparent semiconductor and display components, as well as biomedical imaging.
KAIST (President Choongsik Bae) announced on August 6 that a research team led by Professor Mooseok Jang from the Department of Bio and Brain Engineering has developed a single-shot phase imaging technique that reconstructs a phase object — a transparent object such as glass, plastic film, or a living cell, which produces almost no visible contrast under an ordinary camera but induces a subtle shift in light called a phase change — from a single measurement, even when the object is fully enclosed between two dynamic scattering layers.
Phase objects are difficult to see with conventional cameras because they show little brightness contrast with their surroundings. However, analyzing the minute phase shift can reveal an object's morphology and optical thickness, and can be used to determine its physical thickness or refractive-index variation when the other quantity is known. For this reason, phase imaging is widely used to observe living cells without staining and to inspect transparent components in semiconductors and displays.
The challenge is that when scattering layers positioned in front of and behind an object are in motion — much like the blurred view through a foggy window — the light path continually changes, making it difficult to obtain accurate information about the object. Conventional techniques have therefore required multiple exposures of the same target, prior calibration of the scattering environment, or training an AI model on large volumes of data.
To address this, the team tightly focused the illumination onto a small spot on the first scattering layer —much like concentrating light to a point with a magnifying glass— so that the light passing through it would carry the object's information as reliably as possible.
The researchers then combined an optical model, which computes how light changes as it passes through the object and scattering layers, with an AI framework. Rather than training on a large set of reference images as conventional AI approaches do, the framework works backward from physical laws to infer the path the light must have taken to produce the measured pattern.
The process is comparable to recovering a clear image from a single blurred photograph taken in fog. Using this approach, the team succeeded in simultaneously determining the shape and thickness of a transparent object, the scattering-induced blur characteristics, and the object's position — all from a single image measuring light intensity.
The technology is expected to have applications in a wide range of fields, including precision inspection for semiconductors and displays and biomedical imaging.
"This is the first demonstration of restoring the shape and position of a transparent object from a single measurement, even in environments where light is severely scattered, such as behind fog or a diffusive film," said Professor Jang. He added that the team plans to develop the technique further so that it operates reliably in more complex environments, with applications in semiconductor inspection and biomedical imaging.
The study was co-first-authored by Yoosun Kim, a master's student, and Gookho Song, a PhD candidate, both in the KAIST Department of Bio and Brain Engineering, with Professor Jang serving as corresponding author. The paper was published in the international optics journal Optica.
Paper title: Single-shot imaging of phase objects fully enclosed by dynamic scattering layers
DOI: https://doi.org/10.1364/OPTICA.593328
This research was supported by the National Research Foundation of Korea under the Ministry of Science and ICT (RS-2021-NR060086, RS-2023-00251628, RS-2026-25479811), and by a Samsung Electronics industry–academia strategic project (IO260313-15915-01).
2026.08.06 View 2779 -
KAIST brings ‘giant batteries’ closer to commercialization in the AI data center era
The explosive growth of AI data centers has brought the commercialization of "giant batteries" one step closer. A KAIST research team has developed a process that cuts the production time for a core material used in large-capacity batteries by 67%, resolving the largest production bottleneck standing in the way of commercialization.
KAIST (President Choongsik Bae) announced on August 5 that a research team led by Professor Hee-Tak Kim from the Department of Chemical and Biomolecular Engineering has developed a process for producing the core electrolyte of vanadium redox flow batteries (VRFBs)—a leading candidate for large-capacity energy storage systems (ESS)—faster and more stably.
As AI data centers operate around the clock in growing numbers, large-capacity ESS that can store electricity generated from solar and wind power and supply it reliably when needed have become increasingly important.
Because VRFBs use nonflammable, water-based electrolytes, they have a lower fire risk than many conventional battery systems. And their energy-storage capacity can be scaled by increasing the amount of electrolyte stored in external tanks. This has drawn attention to VRFBs as ultra-large batteries suited to AI data centers and renewable energy storage. However, producing the vanadium electrolyte with an average oxidation state of 3.5+—the standard starting composition for VRFB operation— has been slow and costly, making it a critical obstacle to commercialization.
The conventional process first produces the electrolyte through chemical reduction—a reaction in which a chemical reducing agent causes vanadium ions to gain electrons—and then refines it through electrochemical reduction, which applies electric current to adjust the vanadium ions' electron state to the desired level. This final electrochemical step, however, relies on a costly VRFB stack and significant electrical energy, increasing both operational complexity and capital costs.Beyond the limitations of the electrochemical reduction process, the research team found, for the first time, that the alternative chemical reduction process also suffers from a distinct kinetic bottleneck. The reaction rate slows sharply at a specific point, much like highway traffic suddenly backing up at a bottleneck. This bottleneck occurs when the average vanadium oxidation state reaches approximately +4.1, an intermediate stage in the production of V3.5+ electrolyte.
In previous research, the team had replaced the conventional electrochemical adjustment step with a Pt/C-catalyzed reduction process, preventing the waste of leftover electrolyte. In the present study, it further extended the catalytic process into the bottleneck region of oxalic-acid-based chemical reduction. By switching from chemical to catalytic reduction at an average oxidation state of approximately +4.1, the team was able to bypass the slowest stage of the production process.
As a result, production time for V3.5+ electrolyte was cut by 67% compared to the conventional process. The switch also eliminated residual oxalic acid, an impurity that can degrade battery performance. The same catalyst was reused more than 2,500 times without a notable drop in performance, demonstrating the process's viability for industrial-scale production.
"This study combined reaction engineering principles with thermodynamic predictions to identify the rate-determining step in the chemical reduction and redesigned the electrolyte production process to overcome this major bottleneck to the commercialization of large-scale batteries," said Hee-Tak Kim, professor in the Department of Chemical and Biomolecular Engineering. He added, "By scientifically identifying the conditions under which the catalyst operates stably without degrading in the electrolyte environment, we resolved a production bottleneck relevant to industry, and we expect this to significantly accelerate the commercialization of large-capacity energy storage technology."
Kyunghwa Seok, a PhD candidate in the Department of Chemical and Biomolecular Engineering, led the research as first author. The findings were published online in Advanced Energy Materials—a leading international journal in the energy field—on May 7. In particular, in recognition of its academic significance, the study was selected as the cover article for Issue 34, which is scheduled to be published online in early September.
Paper title: Streamlined V3.5+ Electrolyte Production by Leveraging Chemical and Catalytic Reductions
DOI: https://doi.org/10.1002/aenm.71029
Authors: Kyunghwa Seok (KAIST, first author), Minseong Kang (KAIST, second author), and Hee-Tak Kim (KAIST, corresponding author).
This research was supported by Lotte Chemical.
2026.08.05 View 3585 -
KAIST Develops Marine Carbon Removal Technology That Turns Carbon Dioxide in Seawater into “Stone” for Permanent Storage
A new pathway has opened to enhance the ocean’s natural ability to clean the planet. KAIST researchers have developed a technology that converts carbon dioxide dissolved in seawater into “stone,” or minerals, preventing it from returning to the atmosphere and enabling permanent storage. The achievement is expected to help the ocean absorb more carbon dioxide and accelerate the commercialization of next-generation marine carbon removal technologies.
KAIST (President Choongsik Bae) announced that a research team led by Professor Dong-Yeun Koh from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor T. Alan Hatton’s group at the Massachusetts Institute of Technology (MIT), has developed an electrochemical dissolved ocean carbon removal (e-DOC) technology that converts carbon dioxide dissolved in seawater into calcium carbonate (CaCO₃), a stable mineral form, enabling virtually permanent carbon storage.
The ocean is the planet’s largest carbon reservoir, absorbing about 30% of the carbon dioxide emitted by human activity. Just as water naturally refills a large container when some is removed, removing carbon dioxide from seawater enables the ocean to absorb more carbon dioxide from the atmosphere.
The research team developed a technology that converts dissolved inorganic carbon (DIC), the carbon species dissolved in seawater, into a mineral form that does not return to the atmosphere. Once stored in this form, the carbon is effectively prevented from returning to the air, allowing the ocean to continue absorbing new carbon dioxide. Such technologies are gaining attention as key carbon dioxide removal (CDR) solutions for responding to climate change.
However, conventional technologies have faced a major challenge: mineral scaling. Much like limescale building up inside a kettle, minerals such as calcium carbonate adhere to electrode surfaces and clog the system. As operation continues, performance declines, requiring frequent cleaning or replacement of components and increasing both energy consumption and maintenance costs.
To overcome this issue, the research team developed a hollow fiber electrode assembly (HFEA), a device composed of bundled hollow, thread-like electrodes. In this structure, minerals form outside the electrode surface rather than directly on it, while hydrogen bubbles naturally generated during the reaction act like a brush, continuously cleaning the electrode surface and preventing mineral buildup.
In experiments using Jeju lava seawater, the team successfully operated the device continuously and stably for more than 120 hours. The system removed 80–90% of dissolved inorganic carbon from seawater and reduced electricity consumption by up to 54% compared with existing technologies. In addition, the process simultaneously produced high-purity hydrogen (H₂) and magnesium hydroxide (Mg(OH)₂), a material used in eco-friendly products and industrial applications, further improving its economic potential.
The newly developed device can be produced in a compact, modular form, making it suitable for installation on ships, offshore plants, and other marine industrial facilities. The research team expects the technology to be scaled up into large-scale marine carbon removal systems that can contribute to achieving carbon neutrality and responding to climate change.
Professor Dong-Yeun Koh said, “This technology converts carbon dioxide dissolved in seawater into a mineral form that does not return to the atmosphere, enabling permanent storage and helping the ocean continuously absorb new carbon dioxide,” adding, “We expect this work to accelerate the commercialization of marine carbon removal technologies and contribute to the realization of a carbon-neutral society.”
This study was co-led by KAIST Ph.D. candidate Inhwan Park of the Department of Chemical and Biomolecular Engineering and Dr. Young Hun Lee of MIT, who received his Ph.D. from KAIST in 2023 and is currently affiliated with the Department of Chemical Engineering at MIT, as co-first authors. The paper was published online on June 19, 2026, in the international journal Advanced Energy Materials.
Paper title: A Compact Hollow Fiber Electrode Assembly Architecture for Continuous Electrochemical Marine Carbon Dioxide Removal
DOI: https://doi.org/10.1002/aenm.71205
This research was supported by Hyundai Motor Company and Kia, as well as the Global C.L.E.A.N. Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.
2026.08.04 View 3116