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KAIST Solves Computer Problems That Would Take Thousands of Years Using Semiconductors
<(From Left) Professor Yang-Kyu Choi, Ph.D. candidate Seong-Yun Yun, (Upper Right) Professor Sanghyeon Kim, Dr. Joon Pyo Kim>
In the era of big data and artificial intelligence, a new approach has emerged for solving combinatorial optimization problems, which involve finding the most efficient solution among many possible options and can otherwise take thousands of years to compute. A KAIST research team has developed computational hardware that can be implemented entirely using existing silicon processes, enabling deployment on existing fabrication lines without additional facilities. This is expected to enable faster and more accurate decision-making across various industries, including logistics, finance, and semiconductor design.
KAIST (President Kwang-Hyung Lee) announced on the 6th of May that a joint research team led by Professor Yang-Kyu Choi and Professor Sanghyeon Kim from the School of Electrical Engineering has implemented an oscillatory Ising machine (a specialized-purpose computer in which multiple oscillating elements interact to find optimal solutions)—a next-generation specialized optimization hardware—using only conventional silicon semiconductor processes.
The research team focused on oscillators that repeat electrical signals periodically. As multiple oscillators exchange signals and synchronize their rhythms, the system naturally reaches the most stable state, and in this process, it finds the optimal solution.
Conventional oscillatory Ising machines have limitations in solving complex problems because it is difficult to precisely control slight frequency differences among oscillators, and the connectivity between elements is limited.
<An aging machine using a silicon oscillator and coupler>
To overcome this, the research team introduced a new approach in which both the oscillators and the couplers are implemented using single silicon transistors, which are the fundamental switching elements of semiconductors.
Through this approach, they reduced frequency deviations among oscillators, enabling stable synchronization, and by using couplers, they implemented multi-level coupling, allowing more precise reflection of problem weights.
As a result, both the ability to represent complex optimization problems and the performance of solution search were significantly improved. Using this technology, the research team successfully solved the representative combinatorial optimization problem known as the Max-Cut problem, which involves dividing a network into two groups to maximize connections.
This problem can be directly applied to various industrial fields such as logistics route optimization, financial portfolio construction, and semiconductor circuit placement. A key advantage of this approach is that it uses the CMOS* process currently employed in the semiconductor industry without requiring special materials or non-standard processes. Therefore, the technology suitable for mass production and commercialization on existing semiconductor production lines without additional facility investment.
*CMOS (Complementary Metal-Oxide-Semiconductor): the most standard process technology in modern semiconductor manufacturing, characterized by very low power consumption and low heat generation, and used to produce chips that serve as the “brains” of almost all digital devices, including smartphones and computer CPUs
<(AI-generated image) Concept diagram of an AI-based silicon aging machine>
Professor Yang-Kyu Choi stated, “This research presents an oscillatory Ising machine hardware that secures both scalability and precision by implementing both oscillators and couplers with silicon devices,” adding, “It is expected to be applied to various industrial fields requiring large-scale combinatorial optimization, such as semiconductor design automation, communication network optimization, and resource allocation.” He further noted that, as transistor miniaturization approaches its physical limits and increasingly requires atomic-level control, our group has spent the past decade exploring whether the future of transistors should extend beyond scaling toward the discovery of new functions. Futurist Alvin Toffler famously divided the development of society into three stages, describing the modern transition into a knowledge-based society as the “Third Wave.” In a similar way, the history of transistor technology, which now spans more than 80 years, may also be viewed in three waves. In 1935, Oskar Heil proposed the concept of controlling semiconductor current with an electric field in a British patent. In 1946, William Shockley developed the first solid-state transistor, an achievement that later led to the Nobel Prize. In 1961, Dawon Kahng invented the modern metal–oxide–semiconductor field-effect transistor, or MOSFET, which remains the foundation of today’s mass-produced semiconductor devices. From this perspective, the first wave of transistor technology can be defined as the “switch,” and the second wave as the “amplifier.” Our laboratory proposes a newly identified third wave: the transistor as an “oscillator.” For decades, semiconductor progress has largely been driven by improving the switching and amplification performance of transistors through miniaturization. However, as device fabrication now demands atomic-scale precision, the physical limits of scaling are becoming increasingly apparent. Future transistors therefore require a fundamental paradigm shift—from further miniaturization toward the realization of new functions. The greatest technological significance of this work lies in demonstrating the oscillator as a third fundamental function of the transistor. As a proof of this concept, we experimentally realized a physical Ising machine operating at room temperature.
This research was led by KAIST Ph.D. candidate Seong-Yun Yun and Dr. Joon Pyo Kim as co-first authors, and was published in Science Advances, one of the world’s most prestigious scientific journals, on March 27.
※ Paper title: “Scalable Ising machine composed entirely of Si transistors,” DOI: 10.1126/sciadv.adz2384
This research was supported by the National Research Foundation of Korea through the Next-Generation Intelligent Semiconductor Technology Development Program, the National Semiconductor Research Laboratory Core Technology Development Program, and the PIM Artificial Intelligence Semiconductor Core Technology Development Program.
2026.05.06 View 401 -
AI Computation Enables Clearer Views of the Deep Brain, Bypassing the Need for Expensive Equipment
< Professor Iksung Kang, KAIST >
Observing the depths of a living brain with clarity has traditionally required expensive, high-end equipment. However, a KAIST research team has advanced neuroscience research by developing a physics-based AI computational algorithm that restores blurred images into sharp ones without the need for additional optical measurement hardware.
KAIST (President Kwang Hyung Lee) announced on April 21st that Professor Iksung Kang (School of Electrical Engineering), in collaboration with Professor Na Ji's research team at UC Berkeley, has developed a technology that accurately corrects image aberrations in microscopes used for live biological imaging. Notably, the experimental design and algorithm development – the core components of this technology – were led by Professor Kang during his postdoctoral fellowship in Professor Na Ji’s group. This breakthrough was achieved using Neural Fields — a neural network-based technology that continuously represents 3D spatial structures to simultaneously reconstruct clear images and volumetric forms.
The research team utilized Two-Photon Fluorescence Microscopy, a core technology for observing deep within living biological tissues by using two low-energy photons simultaneously to selectively illuminate specific points. However, as light passes through thick tissue, it bends and scatters, causing the image to become blurred — much like how objects appear distorted underwater. This phenomenon is known as optical aberration.
Previously, correcting these distortions required adding complex and costly hardware, such as wavefront sensors, which measure exactly how much the light path has deviated.
< Framework for Integrated Distortion Correction in Two-Photon Fluorescence Microscopy >
In contrast, the research team developed an algorithm that inversely calculates how light was distorted using only the captured image data and corrects it. In other words, it is a method of restoring image clarity by analyzing blurred photos, without relying on any additional equipment.
The core of this technology is a machine learning algorithm based on the Neural Fields model. This algorithm tracks the distortion process that occurs as light travels, implementing an integrated technology that compensates not only for optical aberrations caused by biological tissue but also for microscopic movements of the living specimen and alignment errors of the microscope itself.
As a result, the team successfully and reliably obtained high-resolution, high-contrast images from deep within biological tissues, without any separate aberration measurement or correction devices.
This research is particularly significant because it overcomes the conventional limitation that “better images require more expensive equipment” by solving the problem through a software-based approach. This is expected to lower the burden of research equipment costs and allow more researchers to perform precise brain observations.
< Comparison of images using a framework that integrates correction for optical aberrations, sample motion, and microscope errors (AI-generated image) >
Professor Iksung Kang stated, “This research opens the way to see more accurately inside living organisms by combining optics and artificial intelligence technology. Moving forward, we plan to develop this into an intelligent optical imaging system where the microscope itself finds the optimal image.”
This study was published on April 13th in Nature Methods, a leading methodology journal in the field of life sciences.
Paper Title: Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields
DOI: 10.1038/s41592-026-03053-6
Authors: Iksung Kang (KAIST, Co-corresponding & First Author), Hyeonggeon Kim, Ryan Natan, Qinrong Zhang, Stella X. Yu, & Na Ji (UC Berkeley, Co-corresponding Author)
2026.04.29 View 345 -
AI Fixed 'Temporal Errors'... Enhancing Reliability in Medical and Legal Fields
<Ph.D candidate Soyeon Kim, (From Left)Jindong Wang (Microsoft; currently at the College of William & Mary), Xing Xie (Microsoft), and Steven Euijong Whang (Professor at KAIST)>
What if ChatGPT answered with the name of a minister from a year ago when asked, "Who was the minister inaugurated last month?" This is a prime example of the limitations of AI that fails to properly reflect the latest information. Our university’s research team has developed a new evaluation technology that automatically reflects changing real-world information while catching "temporal errors" that may appear correct on the surface. This is expected to drastically improve AI reliability.
KAIST announced on April14th that a research team led by Professor Steven Euijong Whang from the School of Electrical Engineering, in joint research with Microsoft Research, has developed a system that automatically evaluates and diagnoses the temporal reasoning capabilities of Large Language Models (LLMs) using temporal database technology.
For AI to earn user trust, the ability to accurately understand real-world information that changes moment by moment is essential. However, existing evaluation methods only checked whether the answer matched or failed to sufficiently reflect complex temporal relationships, making it difficult to properly evaluate various question scenarios occurring in actual environments.
To solve this, the research team introduced "Temporal Database" design theory—which has been verified over the past 40 years—into AI evaluation for the first time. By utilizing the temporal flow and relational structure of data, the core of this technology is the automatic generation of 13 types of complex time-based problems from the database itself, without the need for humans to manually write evaluation questions.
<Schematic Diagram of the Evaluation Framework Proposed in This Study>
In particular, this technology is evaluated as a major innovation because it shifts from the traditional method where humans manually created problems to a method where evaluation questions are automatically generated based on data. Furthermore, by automating the entire process from problem generation to answer derivation and verification based on the database, the burden of maintenance can be drastically reduced without the need to manually modify questions as was previously required.
When real-world information changes, the evaluation questions, answers, and verification criteria are automatically updated simply by updating the corresponding content in the database. While the input of the latest information itself is handled by external data or administrators, this technology is structured to perform the overall evaluation automatically after such data is updated.
Additionally, moving beyond the existing method of simply judging whether the final answer is correct or incorrect, the research team introduced a new metric that verifies the logical validity of dates or periods presented during the answering process. Through this, they achieved a performance improvement in detecting "Temporal Hallucination" phenomena—where an answer appears correct but has the wrong temporal basis—by an average of 21.7% more accurately than before.
Applying this technology can significantly reduce evaluation maintenance costs since only the database needs to be updated when information changes, and it showed an effect of reducing the amount of input data by an average of 51% compared to previous methods.
<Future AI Evaluation System (AI-Generated Image)>
Professor Steven Euijong Whang stated, "This research is an example showing that classical database design theory can play a crucial role in solving the reliability issues of the latest AI. By converting vast amounts of professional data into evaluation resources, we expect this to become a practical foundation for verifying AI performance in various fields such as medicine and law in the future."
Soyeon Kim, a PhD student at KAIST, participated as the lead author of this study, and Jindong Wang (Microsoft Research, currently at William & Mary) and Xing Xie (Microsoft Research) participated as co-authors. The research results will be presented this April at ICLR 2026, the most prestigious academic conference in the field of artificial intelligence.
Paper Title: Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models
Paper Link: https://arxiv.org/abs/2508.02045
Meanwhile, this research was conducted with support from Microsoft Research, the National Research Foundation of Korea, and the Institute for Information & Communications Technology Planning & Evaluation (IITP) Global AI Frontier Lab projects (RS-2024-00469482, RS-2024-00509258).
2026.04.14 View 428 -
Smart OLED Patch Uses Light to Automate Drug Delivery, Doubling Healing Speed
< (Left) Professor Kyung Cheol Choi, Researcher Hyejeong Yeon (Center) Researcher Sohyeon Yu, Dr. Daekyung Sung, Researcher Sangwoo Kim (Right) Researcher Minhyeok Lee, Professor Chan-Su Park >
Instead of applying ointment and attaching a bandage, a ‘smart patch that regulates treatment intensity on its own just by being attached’ has appeared. Our university's research team has developed a ‘self-regulating OLED wound healing patch’ that combines light and drugs to pull up the wound recovery speed by about twice. It is expected to develop into an intelligent treatment technology where light regulates drug release according to the patient's condition in the future.
KAIST announced on the 13th that a research team led by Professor Kyung Cheol Choi of the School of Electrical Engineering, together with Dr. Daekyung Sung of the Korea Institute of Ceramic Engineering and Technology (President Jong-seok Yoon) and Professor Chan-Su Park's team at Chungbuk National University (Acting President Yu-sik Park), developed a ‘self-regulating wound healing patch’ technology that combines Organic Light Emitting Diodes (OLED) and a Drug Delivery System (DDS).
Ointments can cause side effects when overused, and Photobiomodulation (PBM)* treatment, which helps cell regeneration using light, also had limitations in that its effect decreased if the appropriate amount was exceeded. *PBM (Photobiomodulation): A non-invasive treatment method that promotes the recovery of cells and tissues using low-intensity light.
< Schematic diagram of light-drug combined treatment using an OLED patch >
The research team focused on solving the limitations of existing treatment methods, which make it difficult to appropriately regulate treatment intensity. The core of this research is that ‘light regulates the medicine.’ When light is applied, Reactive Oxygen Species (ROS) are generated in the body, and this substance plays a role in stimulating nanoparticles so that drugs are released.
In other words, the amount of reactive oxygen species generated varies according to the intensity of light, and the amount of drug release is naturally regulated accordingly. When light is applied, cell regeneration is promoted, and at the same time, the ROS generated at this time acts as a ‘switch’ so that the drug is automatically released only as much as necessary. It is an ‘intelligent treatment method’ in which the treatment maintains its optimal level on its own even if a person does not regulate it separately. Simply put, it is a ‘self-regulating treatment patch’ where the medicine automatically comes out in an appropriate amount according to the intensity of the light when it is shone.
The research team produced a 630-nanometer (nm) wavelength OLED patch that closely adheres to the skin. This patch was designed to deliver light evenly to induce cell regeneration while releasing an appropriate amount of antioxidant drugs, such as Centella asiatica (commonly known as tiger grass) extract, a plant-derived ingredient well known for its skin regeneration effects.
In addition, it was produced in a wearable form that perfectly adheres to the curves of the skin to reduce light energy loss, and it maintains a temperature of about 31 degrees Celsius even during long-term use, allowing it to be used safely without the risk of low-temperature burns. Stability, maintaining performance for more than 400 hours, was also confirmed, securing the possibility of application to actual medical devices.
The effect was confirmed through experiments. In skin cell experiments, ‘combined treatment’ using light and drugs together showed faster recovery than single treatment. In mouse experiments, the wound recovery rate was 67% as of the 14th day of treatment, recording a healing speed about twice as fast as that of the control group (35%). The quality of healing was also significantly improved, such as skin thickness and barrier protein formation recovering to normal levels.
Professor Kyung Cheol Choi stated, “This research is an example of expanding OLED-based light treatment beyond the level of simply applying it to the role of regulating the treatment, and into a combined treatment platform where drug release is automatically regulated according to the wound status. We plan to develop it into an intelligent treatment technology that can be applied to various wounds and diseases and reacts on its own according to the patient's body condition.”
In this research, Hyejeong Yeon, a doctoral student at the KAIST School of Electrical Engineering, participated as the first author. It was published online in the international academic journal ‘Materials Horizons’ last January and was selected as the Front Cover Paper in March.
※ Paper title: A self-regulating wearable OLED patch for accelerated wound healing via photobiomodulation-triggered drug delivery, DOI: https://doi.org/10.1039/D5MH02129D (Authors: Hyejeong Yeon, Sohyeon Yu, Minhyeok Lee, Sangwoo Kim, Yongjin Park, Hye-Ryung Choi, Won Il Choi, Chang-Hun Huh, Yongmin Jeon, Chan-Su Park, Daekyung Sung, and Kyung Cheol Choi)
< Materials Horizons cover paper image >
This research was conducted with the support of the Future Discovery Convergence Science and Technology Development Program (2021M3C1C3097646) carried out through the National Research Foundation of Korea (NRF) of the Ministry of Science and ICT.
2026.04.13 View 528 -
Excellence Award at the 4th Wonik Next-Generation Engineering Award
< 4th Wonik Next-Generation Engineering Award hosted by the National Academy of Engineering of Korea (NAEK)>
At the 4th Wonik Next-Generation Engineering Award hosted by the National Academy of Engineering of Korea (NAEK), KAIST Ph.D candidate Yehhyun Jo from the Department of Electrical Engineering(Advisor: Professor Hyunjoo J. Lee) and Ph.D candidate Seokjoo Cho from the Department of Mechanical Engineering(Advisor: Prof. Inkyu Park) received Excellence Awards.
Yehhyun Jo was selected in recognition of the development of a system that enables the precise modulation and observation of brain functions by integrating ultrasound neuromodulation technology, MEMS, and biosignal measurement technology. As a leading researcher in ultrasound brain stimulation in Korea, Yehhyun has contributed to the advancement of next-generation neuroengineering research by publishing six SCI(E)-indexed first-author papers.
In acceptance speech, Yehhyun Jo remakred, “It is a great honor to receive the Excellence Award at the Wonik Next-Generation Engineering Award hosted by the National Academy of Engineering of Korea. I believe this award represents not only my personal achievements, but also the collective efforts of my advisor, fellow researchers, and my parents and brother, who have supported my research behind the scenes. Going forward, I will continue to develop and validate technologies grounded firmly in fundamental principles so that engineering innovation can reach real clinical and industrial settings, and I will strive to become a great researcher who contributes to society through responsible research.”
<(From Left) Ph.D candidate Yehhyun Jo, Ph.D candidate Seokjoo Cho>
Seokjoo Cho was selected for developing a wireless multi-modal sensing system based on nano- and micro-fabrication processes for the management of chronic wounds and metabolic diseases. Through this related work, Seokjoo has published 25 SCI(E)-indexed papers and is leading technological innovation in next-generation healthcare sensor platforms.
He accepted the award, saying, “I am sincerely grateful to receive the great honor of the Wonik Next-Generation Engineering Award. Winning an award that I have long dreamed of as a researcher during my graduate studies brings me both deep fulfillment and a strong sense of responsibility. Taking this award as an opportunity, I will continue striving to grow as a researcher who does not lose sight of my original motivation and who can create meaningful value for society.”
The Wonik Next-Generation Engineering Award is presented to undergraduate and graduate students in engineering-related fields in Korea to recognize creative and ambitious future engineers in the materials, components, and equipment sectors and support their growth into engineers who contribute to solving social problems.
The award ceremony was held on the afternoon of March 10 at the Grand Walkerhill Seoul Hotel in Gwangjin-gu, Seoul.
2026.04.01 View 505 -
KAIST Enables Ultra-High-Resolution AR·VR Without Battery Burden
<(From Left) Professor Young Min Song, Ph.D candidate Hyo Eun Jeong, (Upper Left) Professor Hyeon-Ho Jeong, Dr. Joo Hwan Ko>
A new display technology has emerged that significantly increases resolution while consuming almost no power. A Korean research team has developed a “monopixel” structure in which a single pixel can independently change colors while consuming minimal energy to maintain them. This breakthrough opens the possibility of creating sharper AR/VR displays without heavy battery demands.
KAIST (President Kwang Hyung Lee) announced on the 29th of March that a research team led by Professor Young Min Song of the School of Electrical Engineering, in collaboration with Professor Hyeon-Ho Jeong’s team at Gwangju Institute of Science and Technology (GIST, President Ki-Cheol Lim), has developed a new low-power monopixel technology called a “reconfigurable Gires–Tournois resonator (r-GT).” This system uses electrochromic materials—substances that change color when electricity is applied—to produce colors with very low power consumption.
Displays have been making pixels increasingly smaller to achieve higher resolution. However, as pixels shrink, power consumption rises and brightness decreases. This is especially challenging for AR/VR devices, which must achieve both extremely small pixels and low power consumption due to their proximity to the human eye.
The r-GT pixel developed by the research team changes color when voltage is applied, and once changed, the color is maintained for a certain period even after the power is turned off. In other words, power is only required when changing colors, while maintaining color requires almost no energy.
The core of this technology lies in two elements. First is a conductive polymer, polyaniline (PANI), whose properties change when voltage is applied. This material responds even at voltages below 1 volt (V), altering its refractive index and thereby changing color. The refractive index refers to how much light bends when passing through a material, and changes in this value lead to changes in perceived color.
Second, the system incorporates a resonator structure that reflects light multiple times to amplify specific colors. This structure enhances even small changes, enabling vivid color expression with minimal power.
As a result, the system achieved a wide color variation exceeding 220° using ultra-low power (90 μW cm⁻²). In simple terms, it can express more than half of the full color wheel (360°) using only about 0.00009 watts per square centimeter.
Another key feature is the “monopixel” structure. Unlike conventional displays that divide a single pixel into red (R), green (G), and blue (B) subpixels, the monopixel approach allows one pixel to independently produce various colors. This enables more pixels within the same area, resulting in higher resolution and reduced light loss, leading to clearer images.
Additionally, PANI retains its color state even after the applied voltage is removed. This confirms the feasibility of a “memory-in-pixel” display, where energy is used only when changing colors, not when maintaining them.
<Reflective display AI image>
The research team demonstrated that this technology can achieve a wide color range (220.6°) and reduce pixel size to as small as 1.5 micrometers (μm), corresponding to an ultra-high resolution of up to approximately 16,900 PPI—beyond the level where individual pixels can be distinguished by the human eye.
Moreover, even with a single-pixel structure, the system can represent about 48.1% of the standard sRGB color gamut, and up to 69.9% with varied material combinations, enabling richer color expression.
The team fabricated a 5×5 monopixel array to verify performance. The energy required to change colors was extremely low (2.31 mJ), demonstrating up to 5.8 times lower power consumption compared to conventional LEDs. As a reflective display, it also becomes more visible under brighter ambient lighting, since it uses external light rather than emitting its own.
<Structure and Representative Results of an Electrically Tunable Single Reflective Resonant Device Using Conductive Polymers>
This study demonstrates that combining electrochemical materials with optical resonator structures enables full-color implementation at ultra-low power. It is expected to be applied in various fields requiring energy efficiency, including ultra-high-resolution near-eye displays for AR/VR, wearable devices, outdoor displays, and electronic paper. It also suggests the potential for sustainable and energy-efficient display technologies by minimizing power consumption during color retention.
Professor Young Min Song stated, “This technology allows a wide range of color changes using very little electricity,” adding, “When combined with future display driving methods, it could enable not only clearer and more energy-efficient ultra-high-resolution displays but also a variety of optical applications.”
This research was conducted with Hyo Eun Jeong, an integrated M.S./Ph.D. student at KAIST, as co-first author, and Professor Young Min Song as the corresponding author. The results were published online on February 28 in Light: Science & Applications, a leading international journal in optics.
※ Paper title: “Sub-1-volt, reconfigurable Gires-Tournois resonators for full-coloured monopixel array,” DOI: https://www.nature.com/articles/s41377-026-02228-2
This research was supported by multiple programs funded by the Ministry of Science and ICT, the National Research Foundation of Korea (NRF), the InnoCORE-GIST program, nanomaterials and technology development initiatives, future medical innovation programs, international collaboration hubs, and the Ministry of Trade, Industry and Energy (MOTIE).
2026.03.30 View 401 -
KAIST Expands Storage Capacity with Smart Gate Semiconductor Technology
<(From Left) Ph. D candidate Dae Hyun Kang, Professor Byung Jin Cho>
From smartphones to large-scale AI servers, most digital information in modern society is stored in NAND flash memory*. KAIST researchers have developed an innovative technology that can overcome the limitations of next-generation semiconductors, where more data must be stored in smaller spaces. This advancement is expected to serve as a key enabling technology for realizing ultra-high-capacity memory.*NAND flash memory: a non-volatile semiconductor memory used in storage devices such as smartphones and SSDs, where data such as photos, videos, and apps are retained even when power is turned off.
KAIST (President Kwang Hyung Lee) announced on the 20th of March that a research team led by Professor Byung Jin Cho of the School of Electrical Engineering has overcome the scaling limitations of 3D V-NAND memory* by implementing a “smart gate” structure that selectively controls electron movement depending on conditions, using a new material applied to an ultra-thin semiconductor layer thinner than a human hair.*3D V-NAND: a memory technology that stacks memory cells vertically, unlike conventional planar (2D) arrangements, enabling higher data storage density.
This research is particularly significant in that it addresses the longstanding issues of speed degradation and reliability during data write and erase operations by utilizing a novel material called boron oxynitride (BON).
In semiconductor memory, the tunneling layer—a thin insulating layer that acts as a pathway for electrons to move in and out of the memory cell—has historically faced a trade-off between performance and reliability.
With conventional materials, it has been difficult to achieve both simultaneously. For example, the widely used silicon oxynitride (SiON) increases data leakage when the tunneling path is widened to improve erase speed, while narrowing the path to prevent leakage significantly slows down data erasure. This trade-off has been a major obstacle to implementing next-generation penta-level cell (PLC) technology.
PLC technology stores 5 bits of data per memory cell by distinguishing 32 different voltage states, allowing much higher data density within the same physical size.
To overcome this limitation, the research team introduced BON, a completely new material beyond conventional silicon-based systems, into the tunneling layer. This material exhibits a unique physical property in which the energy barrier height differs depending on the type of charge carrier.
Leveraging this property, the team designed an asymmetric energy barrier structure that allows holes (positive charge carriers)—needed for data erase—to pass through easily, while blocking electrons, which represent stored data, from leaking out.
An asymmetric energy barrier refers to a structure in which the energy required for charge carriers to move varies depending on the type of charge. This enables efficient charge transport during erase operations while effectively preventing data loss. The concept is analogous to a “smart gate” that opens easily for entry but firmly blocks exit, implemented at the semiconductor level.
Experimental results showed that devices using the BON tunneling layer achieved up to a 23-fold improvement in erase speed compared to conventional technologies and demonstrated excellent durability with minimal performance degradation even after tens of thousands of operation cycles.
Notably, even under the highly demanding PLC operation—where 32 distinct voltage levels must be precisely controlled—the researchers achieved more than threefold improvement in controlling data distribution across devices.
< Schematic diagram of the asymmetric energy barrier structure and operating principle of the BON tunneling layer >
This achievement is considered by both academia and industry to be beyond a purely experimental result, reaching a level immediately applicable to real semiconductor manufacturing processes.
Professor Byung Jin Cho stated, “This research presents a novel technology that can be directly applied to the production of next-generation ultra-high-capacity memory,” adding, “It will significantly contribute to maintaining Korea’s technological leadership in the semiconductor industry.”
This study was implemented by Dae Hyun Kang, an integrated master’s–PhD student in Electrical Engineering, as the first author. The research was presented at the IEEE International Electron Devices Meeting (IEDM) on December 9, one of the most prestigious conferences in the semiconductor field, attracting global attention.
The work also received the Grand Prize (first place overall in the university category) at the 32nd Samsung Human Tech Paper Awards, marking a notable achievement as a traditional semiconductor device study in a competition typically dominated by AI-related research.
※ Paper title: “Bandgap-Engineered Boron Oxynitride Tunneling Layer for Reliable PLC Operation of 3D V-NAND Flash Memory Devices,” DOI: https://doi.org/10.1109/IEDM50572.2025.11353681
This research was supported by the National Semiconductor Research Lab Core Technology Development Program funded by the Ministry of Science and ICT.
2026.03.23 View 662 -
KAIST Overcomes Limitations of Existing Image Sensors… Clear Colors Even Under Oblique Light
<(From Left) Ph.D candidate Chanhyung Park from Electrical Engineering, Jaehyun Jeon from Department of Physics, Professor Min Seok Jang from Electrical Engineering>
Smartphone cameras are becoming smaller, yet photos are becoming sharper. Korean researchers have elevated the limits of next-generation smartphone cameras by developing a new image sensor technology that can accurately represent colors regardless of the angle at which light enters. The team achieved this by utilizing a “metamaterial” that designs the movement of light through structures too small to be seen with the naked eye.
KAIST (President Kwang Hyung Lee) announced on the 12th of February that a research team led by Professor Min Seok Jang of the School of Electrical Engineering, in collaboration with Professor Haejun Chung’s team at Hanyang, has developed a metamaterial-based technology for image sensors that can stably separate colors even when the angle of light incidence varies.
Conventional smartphone cameras capture images by concentrating light into a small lens. However, as camera pixels become extremely small, lenses alone struggle to gather sufficient light. To address this, the Nanophotonic Color Router was introduced. Instead of concentrating light through a lens, this technology uses microscopic structures invisible to the eye to precisely separate incoming light by color. By designing the pathways through which light travels, this metamaterial-based structure accurately divides light into red (R), green (G), and blue (B).
Samsung Electronics has already demonstrated the commercialization potential of this technology by applying it to actual image sensors under the name “Nano Prism.” Theoretically, stacking multiple layers of extremely fine nanostructures enables greater light collection and more accurate color separation.
<Nanophotonic color router technology that works reliably even under oblique incidence conditions (AI-generated image)>
However, existing Nanophotonic Color Routers had limitations. While they functioned well when light entered vertically, their performance deteriorated significantly—or colors mixed—when light entered at an angle, as is common in smartphone cameras. This issue, known as the “oblique incidence problem,” has been considered a critical challenge that must be resolved for real-world product applications.
The research team first investigated the root cause of this issue. They found that previous designs were overly optimized for vertically incident light, causing performance to drop sharply even with slight changes in the angle of incidence. Since smartphone cameras receive light from various angles, maintaining performance under angular variation is essential.
Instead of manually designing the structure, the team adopted an “inverse design” approach, which allows the computer to autonomously determine the optimal structure. Through this method, they derived a color router design capable of stable color separation even when the angle of incoming light changes.
As a result, whereas previous structures nearly failed when light was tilted by about 12 degrees, the newly designed structure maintained approximately 78% optical efficiency within a ±12-degree range, demonstrating stable color separation performance. In other words, the technology reaches a level suitable for practical smartphone usage environments.
<Nanophotonic color router robust to oblique incidence>
The team further analyzed performance variations by considering factors such as the number of metamaterial layers, design conditions, and potential fabrication errors. They also systematically defined the limits of robustness against changes in the angle of incidence. This study is particularly meaningful in that it presents design criteria for color routers that reflect realistic image sensor environments.
Professor Min Seok Jang of KAIST stated, “This research is significant in that it systematically analyzes the oblique incidence problem, which has hindered the commercialization of color router technology, and proposes a clear solution direction,” adding, “The proposed design methodology can be extended beyond color routers to a wide range of metamaterial-based nanophotonic devices.”
In this study, KAIST undergraduate student Jaehyun Jeon and doctoral candidate Chanhyung Park participated as co-first authors. The research findings were published on January 27 in the international journal Advanced Optical Materials.
※ Paper title: “Inverse Design of Nanophotonic Color Router Robust to Oblique Incidence”
DOI: https://doi.org/10.1002/adom.202501697※ Authors: Jaehyun Jeon (KAIST, first author), Chanhyung Park (KAIST, first author), Doyoung Heo (KAIST), Haejun Chung (Hanyang University), Min Seok Jang (KAIST, corresponding author)
This research was supported by the Ministry of Trade, Industry & Energy (Korea Institute for Advancement of Technology, Korea Semiconductor Research Consortium) under the project “Design Technology of Meta-Optical Structures for Next-Generation Sensors,” by the Ministry of Science and ICT (National Research Foundation of Korea) under the projects “Development of Full-Color Micro LED Devices and Panels Based on Beam-Steerable High-Color-Purity Meta Color Conversion Layers” and “Development of a Real-Time Zero-Energy Argos-Eye Metasurface Network Computing with All Properties of Light,” and by the Ministry of Culture, Sports and Tourism (Korea Creative Content Agency) under the project “International Joint Research for Next-Generation Copyright Protection and Secure Content Distribution Technologies.”
2026.02.19 View 1600 -
KAIST Solves Key Micro-LED Challenges, Enabling Reality Like Visuals for AR/VR Devices
<(Back row, from left) Dr. Juhyuk Park, Ph.D candidate Hyunsu Ki, (Front row, from left) M.S candidate Haoi Le Bao, M.S candidate Chaeyeon Kim, (Circled, from left) Prof. Sanghyeon Kim, Prof. Dae-Myeong Keum >
From TVs and smartwatches to rapidly emerging VR and AR devices, micro-LEDs are a next-generation display technology in which each LED—smaller than the thickness of a human hair—emits light on its own. Among the three primary colors required for full-color displays—red, green, and blue—the realization of high-performance red micro-LEDs has long been considered the most difficult. KAIST researchers have now successfully demonstrated a high-efficiency, ultra-high-resolution red micro-LED display, paving the way for displays that can deliver visuals even sharper than reality.
KAIST (President Kwang Hyung Lee) announced on the 28th that a research team led by Professor Sanghyeon Kim of the School of Electrical Engineering, in collaboration with Professor Dae-Myeong Geum of Inha University, compound-semiconductor manufacturer QSI, and microdisplay/SoC design company Raontech, has developed a red micro-LED display technology that achieves ultra-high resolution while significantly reducing power consumption.
Using this technology, the team successfully demonstrated a 1,700 PPI* class ultra-high-resolution micro-LED display—approximately 3–4 times higher than the resolution of current flagship smartphone displays—capable of delivering truly “reality-like” visuals even in VR and AR devices.
*PPI (Pixels Per Inch): indicates how densely pixels are packed on a display; higher PPI corresponds to finer image detail.
Micro-LEDs are self-emissive displays that surpass OLEDs in brightness, lifetime, and energy efficiency, but they have faced two major technical challenges. The first is the efficiency degradation of red micro-LEDs, which becomes severe as pixel sizes shrink due to increased energy leakage. The second is the limitation of conventional transfer processes, which rely on mechanically locating and placing countless microscopic LEDs one by one, making ultra-high-resolution fabrication difficult and increasing defect rates.
<Results of Red Micro-LED Performance Improvement>
The research team addressed both challenges simultaneously. First, they adopted an AlInP/GaInP quantum-well structure, enabling highly efficient red micro-LEDs with minimal energy loss even at very small pixel sizes. Simply put, the quantum well/barrier structure acts as an “energy barrier.” It confines electrons and holes within the quantum well layer, preventing carrier leakage. By adopting quantum wells with higher hole concentration, the research team effectively reduced energy loss as pixel sizes decreased, enabling brighter and more efficient red micro-LEDs.
Also, instead of transferring individual LEDs, the researchers employed a monolithic three-dimensional (3D) integration technique, stacking the LED layers directly on top of the driving circuitry. This approach minimizes alignment errors, reduces defect rates, and enables stable fabrication of ultra-high-resolution displays. The team also developed a low-temperature process to prevent damage to the underlying circuitry during integration.
<Monolithic 3D MicroLED-on-Si Display>
This achievement is particularly significant because it demonstrates a fully functional, ultra-high-resolution, and highly-quantum-efficient red micro-LED display, widely regarded as the most difficult component to realize. The technology is expected to find broad applications in next-generation displays where pixel granularity must be virtually imperceptible, including AR/VR smart glasses, automotive head-up displays (HUDs), and ultra-compact wearable devices.
Professor Sanghyeon Kim commented, “This work simultaneously solves the long-standing challenges of red pixel efficiency and circuit integration in micro-LEDs. We will continue to advance this technology toward practical commercialization as a next-generation display platform.”
The study was led by Dr. Juhyuk Park of the KAIST Institute of Information Electronics as first author, and the results were published on January 20 in the international journal Nature Electronics.
※ Paper title: “A Monolithic Three-Dimensional Integrated Red Micro-LED Display on Silicon Using AlInP/GaInP Epilayers” ※ DOI: 10.1038/s41928-025-01546-4
This research was supported by the National Research Foundation of Korea Basic Research Program (2019), the Display Strategic Research Laboratory Program (currently ongoing), and the Samsung Future Technology Incubation Center (2020-2023).
<Monolithic 3D Direct Technology (AI-generated image)>
2026.01.30 View 1212 -
Presenting a Brain-Like Next-Generation AI Semiconductor that Sees and Judges Instantly
< (From left) Professor Sanghun Jeon, Ph.D candidate Seungyeob Kim, Postdoctoral researcher Hongrae Cho, Ph.D candidates Sang-ho Lee and Taeseung Jung, and M.S candidate Seonjae Park >
With the advancement of Artificial Intelligence (AI), the importance of ultra-low-power semiconductor technology that integrates sensing, computation, and memory into a single unit is growing. However, conventional structures face challenges such as power loss due to data movement, latency, and limitations in memory reliability. A Korean research team has drawn international academic attention by presenting core technologies for an integrated ‘Sensor–Compute–Store’ AI semiconductor to solve these issues.
KAIST announced on December 31st that Professor Sanghun Jeon’s research team from the School of Electrical Engineering presented a total of six papers at the ‘International Electron Devices Meeting (IEEE IEDM 2025)’—the world’s most prestigious semiconductor conference—held in San Francisco from December 8 to 10. Among these, the papers were simultaneously selected as a Highlight Paper and a Top Ranked Student Paper.
Highlight Paper: Monolithically Integrated Photodiode–Spiking Circuit for Neuromorphic Vision with In-Sensor Feature Extraction [Link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=255]
Top Ranked Student Paper: A Highly Reliable Ferroelectric NAND Cell with Ultra-thin IGZO Charge Trap Layer; Trap Profile Engineering for Endurance and Retention Improvement [Link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=124]
The research on the M3D integrated neuromorphic vision sensor, selected as a highlight paper, is a semiconductor that stacks the human eye and brain within a single chip. Simply put, the sensors that detect light and the circuits that process signals like a brain are made into very thin layers and stacked vertically in one chip, implementing a structure where the process of 'seeing' and 'judging' occurs simultaneously.
Through this, the research team completed the world's first "In-Sensor Spiking Convolution" platform, where AI computation technology that "sees and judges at the same time" takes place directly within the camera sensor.
< Figure 1. Summary of research on vertically stacked optical signal-to-spike frequency converter for AI >
< Figure 2. Representative diagram of the development of a 2T-2C near-pixel analog computing cell based on oxide thin-film transistors >
Previously, this technology required several stages: capturing an image (sensor), converting it to digital (ADC), storing it in memory (DRAM), and then calculating (CNN). However, this new technology eliminates unnecessary data movement as the calculation happens immediately within the sensor. As a result, it has become possible to implement real-time, ultra-low-power Edge AI with significantly reduced power consumption and dramatically improved response speeds.
Based on this approach, the research team presented six core technologies at the conference covering all layers of AI semiconductors, from input to storage. They simultaneously created neuromorphic semiconductors that operate like the brain using much less electricity while utilizing existing semiconductor processes, along with next-generation memory optimized for AI.
First, on the sensor side, they designed the system so that judgment occurs at the sensor stage rather than having separate components for capturing images and calculating. Consequently, power consumption decreased and response speeds increased compared to the conventional method of taking a photo and sending it to another chip for calculation.
< Figure 3. Schematic diagram of a next-generation biomimetic tactile system using neuromorphic devices >
< Figure 4. Representative diagram of NC-NAND development research based on Ultra-thin-Mo and Sub-3.5 nm HZO >
Furthermore, in the field of memory, they implemented a next-generation NAND flash that uses the same materials but operates at lower voltages, lasts longer, and can store data stably even when the power is turned off. Through this, they presented a foundational technology that satisfies the requirements for high-capacity, high-reliability, and low-power memory necessary for AI.
< Figure 5. Representative diagram of next-generation 3D FeNAND memory development research >
< Figure 6. Representative diagram of research on charge behavior characterization and quantitative analysis methodology for next-generation FeNAND memory >
Professor Sanghun Jeon, who led the research, stated, "This research is significant in that it demonstrates that the entire hierarchy can be integrated into a single material and process system, moving away from the existing AI semiconductor structure where sensing, computation, and storage were designed separately." He added, "Moving forward, we plan to expand this into a next-generation AI semiconductor platform that encompasses everything from ultra-low-power Edge AI to large-scale AI memory."
Meanwhile, this research was conducted with support from basic research projects of the Ministry of Science and ICT and the National Research Foundation of Korea, as well as the Center for Heterogeneous Integration of Extreme-scale & Property Semiconductors (CH³IPS). It was carried out in collaboration with Samsung Electronics, Kyungpook National University, and Hanyang University.
2025.12.31 View 1628 -
Turning PC and Mobile Devices into AI Infrastructure, Reducing ChatGPT Costs
< (From left) KAIST School of Electrical Engineering: Dr. Jinwoo Park, M.S candidate Seunggeun Cho, and Professor Dongsu Han >
Until now, AI services based on Large Language Models (LLMs) have mostly relied on expensive data center GPUs. This has resulted in high operational costs and created a significant barrier to entry for utilizing AI technology. A research team at KAIST has developed a technology that reduces reliance on expensive data center GPUs by utilizing affordable, everyday GPUs to provide AI services at a much lower cost.
On December 28th, KAIST announced that a research team led by Professor Dongsu Han from the School of Electrical Engineering developed 'SpecEdge,' a new technology that significantly lowers LLM infrastructure costs by utilizing affordable, consumer-grade GPUs widely available outside of data centers.
SpecEdge is a system where data center GPUs and "edge GPUs"—found in personal PCs or small servers—collaborate to form an LLM inference infrastructure. By applying this technology, the team successfully reduced the cost per token (the smallest unit of text generated by AI) by approximately 67.6% compared to methods using only data center GPUs.
To achieve this, the research team utilized a method called 'Speculative Decoding.' In this process, a small language model placed on the edge GPU quickly generates a high-probability token sequence (a series of words or word fragments). Then, the large-scale language model in the data center verifies this sequence in batches. During this process, the edge GPU continues to generate words without waiting for the server's response, simultaneously increasing LLM inference speed and infrastructure efficiency.
< Figure 1. Language data flow diagram of the developed SpecEdge >
< Figure 2. Detailed computation time reduction method of SpecEdge >
< Figure 3. Illustration of efficient batching of verification requests from multiple edge GPUs on the server GPU within SpecEdge >
Compared to performing speculative decoding solely on data center GPUs, SpecEdge improved cost efficiency by 1.91 times and server throughput by 2.22 times. Notably, the technology was confirmed to work seamlessly even under standard internet speeds, meaning it can be immediately applied to real-world services without requiring a specialized network environment.
Furthermore, the server is designed to efficiently process verification requests from multiple edge GPUs, allowing it to handle more simultaneous requests without GPU idle time. This has realized an LLM serving infrastructure structure that utilizes data center resources more effectively.
This research presents a new possibility for distributing LLM computations—which were previously concentrated in data centers—to the edge, thereby reducing infrastructure costs and increasing accessibility. In the future, as this expands to various edge devices such as smartphones, personal computers, and Neural Processing Units (NPUs), high-quality AI services are expected to become available to a broader range of users.
< Figure 4. Conceptual comparison of the developed SpecEdge vs. conventional methods >
Professor Dongsu Han, who led the research, stated, "Our goal is to utilize edge resources around the user, beyond the data center, as part of the LLM infrastructure. Through this, we aim to lower AI service costs and create an environment where anyone can utilize high-quality AI."
Dr. Jinwoo Park and M.S candidate Seunggeun Cho from KAIST participated in this study. The research results were presented as a 'Spotlight' (top 3.2% of papers, with a 24.52% acceptance rate) at the NeurIPS (Neural Information Processing Systems) conference, the world's most prestigious academic conference in the field of AI, held in San Diego from December 2nd to 7th.
Paper Title: SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs
Paper Links: NeurIPS Link, arXiv Link
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the project 'Development of 6G System Technology to Support AI-Native Application Services.'
2025.12.29 View 2142 -
KAIST Researchers First in the World to Identify Security Threat Exploiting Google Gemini’s "Malicious Expert AI" Structure
<Photo 1. (From left) Ph.D. candidates Mingyoo Song and Jaehan Kim, Professor Sooel Son, (Top right) Professor Seungwon Shin, Lead Researcher Seung Ho Na>
Most major commercial Large Language Models (LLMs), such as Google’s Gemini, utilize a Mixture-of-Experts (MoE) structure. This architecture enhances efficiency by dynamically selecting and using multiple "small AI models (Expert AIs)" depending on input queries . However, KAIST research team has revealed for the first time in the world that this very structure can actually become a new security threat.
A joint research team led by Professor Seungwon Shin (School of Electrical Engineering) and Professor Sooel Son (School of Computing) announced on December 26th that they have identified an attack technique that can seriously compromise the safety of LLMs by exploiting the MoE structure. For this research, they received the Distinguished Paper Award at ACSAC 2025, one of the most prestigious international conferences in the field of information security.
ACSAC (Annual Computer Security Applications Conference) is among the most influential international academic conferences in security. This year, only two papers out of all submissions were selected as Distinguished Papers. It is highly unusual for a domestic Korean research team to achieve such a feat in the field of AI security.
In this study, the team systematically analyzed the fundamental security vulnerabilities of the MoE structure. In particular, they demonstrated that even if an attacker does not have direct access to the internal structure of a commercial LLM, the entire model can be induced to generate dangerous responses if just one maliciously manipulated "Expert Model" is distributed through open-source channels and integrated into the system.
<Figure 1. Conceptual diagram of the attack technology proposed by the research team.>
To put it simply: even if there is only one "malicious expert" mixed among normal AI experts, that specific expert may be repeatedly selected for processing harmful queries, causing the overall safety of the AI to collapse. A particularly dangerous factor highlighted was that this process causes almost no degradation in model performance, making the problem extremely difficult to detect in advance.
Experimental results showed that the attack technique proposed by the research team could increase the harmful response rate from 0% to up to 80%. They confirmed that the safety of the entire model significantly deteriorates even if only one out of many experts is "infected."
This research is highly significant as it presents the first new security threat that can occur in the rapidly expanding global open-source-based LLM development environment. Simultaneously, it suggests that verifying the "source and safety of individual expert models" is now essential—not just performance—during the AI model development process.
Professors Seungwon Shin and Sooel Son stated, "Through this study, we have empirically confirmed that the MoE structure, which is spreading rapidly for the sake of efficiency, can become a new security threat. This award is a meaningful achievement that recognizes the importance of AI security on an international level."
The study involved Ph.D. candidates Jaehan Kim and Mingyoo Song, Dr. Seung Ho Na (currently at Samsung Electronics), Professor Seungwon Shin, and Professor Sooel Son. The results were presented at ACSAC in Hawaii, USA, on December 12, 2025.
<Figure 2. Photo of the Distinguished Paper Award certificate>
Paper Title: MoEvil: Poisoning Experts to Compromise the Safety of Mixture-of-Experts LLMs
Paper File: https://jaehanwork.github.io/files/moevil.pdf
GitHub (Open Source): https://github.com/jaehanwork/MoEvil
This research was supported by the Korea Internet & Security Agency (KISA) and the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Ministry of Science and ICT.
2025.12.26 View 2835