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Breaking the 1% Barrier, KAIST Boosts Brightness of Eco-Friendly Ultra-Small Semiconductors by 18-Fold
<(Front rwo, from left) KAIST co-first author Changhyun Joo, co-first author Seongbeom Yeon, (Back row, from left) Jaeyoung Ha, Professor Himchan Cho, Jaedong Jang>
Light-emitting semiconductors are used throughout everyday life in TVs, smartphones, and lighting. However, many technical barriers remain in developing environmentally friendly semiconductor materials. In particular, nanoscale semiconductors that are tens of thousands of times smaller than the width of a human hair (about 100,000 nanometers) are theoretically capable of emitting bright light, yet in practice have suffered from extremely weak emission. KAIST researchers have now developed a new surface-control technology that overcomes this limitation.
KAIST (President Kwang Hyung Lee) announced on the 14th of January that a research team led by Professor Himchan Cho of the Department of Materials Science and Engineering has developed a fundamental technology to control, at the atomic level, the surface of indium phosphide (InP)* magic-sized clusters (MSCs)—nanoscale semiconductor particles regarded as next-generation eco-friendly semiconductor materials.* Indium phosphide (InP): a compound semiconductor made of indium (In) and phosphorus (P), considered an environmentally friendly alternative that does not use hazardous elements such as cadmium
The material studied by the team is known as a magic-sized cluster, an ultrasmall semiconductor particle composed of only several tens of atoms. Because all particles have identical size and structure, these materials are theoretically capable of emitting extremely sharp and pure light. However, due to their extremely small size of just 1–2 nanometers, even minute surface defects cause most of the emitted light to be lost. As a result, luminescence efficiency has remained below 1% to date.
Previously, this issue was addressed by etching the surface with strong chemicals such as hydrofluoric acid (HF). However, the overly aggressive reactions often damaged the semiconductor itself.
Professor Cho’s team adopted a different approach. Instead of removing the surface all at once, they devised a precision etching strategy that allows chemical reactions to proceed in a highly controlled, incremental manner. This enabled selective removal of only the defect sites that hindered light emission, while preserving the overall structure of the semiconductor. During this defect-removal process, fluorine generated by the reaction combined with zinc species in the solution to form zinc chloride, which in turn stabilized and passivated the exposed nanocrystal surface.
< Schematic illustration of overcoming emission efficiency limits via atomic-scale precision control >
As a result, the research team increased the luminescence efficiency of the semiconductor from below 1% to 18.1%. This represents the highest reported performance to date among indium phosphide–based ultrasmall nanosemiconductors, corresponding to an 18-fold increase in brightness.
This study is particularly significant in that it demonstrates, for the first time, that the surfaces of ultrasmall semiconductors—previously considered nearly impossible to control—can be precisely engineered at the atomic level. The technology is expected to find applications not only in next-generation displays, but also in advanced fields such as quantum communication and infrared sensing.
< Eco-friendly Ultra-compact Semiconductor Chemical Reaction (AI-generated image) >
Professor Himchan Cho explained, “This work is not simply about making brighter semiconductors, but about demonstrating how critical atomic-level surface control is for achieving desired performance.”
This research was carried out with Changhyun Joo, a doctoral student, and Seongbeom Yeon, a combined master’s-doctoral student in the Department of Materials Science and Engineering at KAIST, serving as co–first authors. Professor Himchan Cho and Professor Ivan Infante of the Basque Center for Materials, Applications, and Nanostructures (BCMaterials, Spain) participated as co-corresponding authors. The study was published online on December 16 in the Journal of the American Chemical Society (JACS), one of the most prestigious journals in chemistry.
※ Paper title: “Overcoming the Luminescence Efficiency Limitations of InP Magic-Sized Clusters,” DOI: 10.1021/jacs.5c13963
This research was supported by the National Research Foundation of Korea through the Nano Materials Technology Development Program, the Next-Generation Intelligent Semiconductor Technology Development Program, the Quantum Information Science Human Infrastructure Program, and by the Korea Basic Science Institute through its Infrastructure Support Program for Early-Career Researchers.
2026.01.14 View 132 -
KAIST detects ‘hidden defects’ that degrade semiconductor performance with 1,000× higher sensitivity
<(From Left) Professor Byungha Shin, Ph.D candidate Chaeyoun Kim, Dr. Oki Gunawan>
Semiconductors are used in devices such as memory chips and solar cells, and within them may exist invisible defects that interfere with electrical flow. A joint research team has developed a new analysis method that can detect these “hidden defects” (electronic traps) with approximately 1,000 times higher sensitivity than existing techniques. The technology is expected to improve semiconductor performance and lifetime, while significantly reducing development time and costs by enabling precise identification of defect sources.
KAIST (President Kwang Hyung Lee) announced on January 8th that a joint research team led by Professor Byungha Shin of the Department of Materials Science and Engineering at KAIST and Dr. Oki Gunawan of the IBM T. J. Watson Research Center has developed a new measurement technique that can simultaneously analyze defects that hinder electrical transport (electronic traps) and charge carrier transport properties inside semiconductors.
Within semiconductors, electronic traps can exist that capture electrons and hinder their movement. When electrons are trapped, electrical current cannot flow smoothly, leading to leakage currents and degraded device performance. Therefore, accurately evaluating semiconductor performance requires determining how many electronic traps are present and how strongly they capture electrons.
The research team focused on Hall measurements, a technique that has long been used in semiconductor analysis. Hall measurements analyze electron motion using electric and magnetic fields. By adding controlled light illumination and temperature variation to this method, the team succeeded in extracting information that was difficult to obtain using conventional approaches.
Under weak illumination, newly generated electrons are first captured by electronic traps. As the light intensity is gradually increased, the traps become filled, and subsequently generated electrons begin to move freely. By analyzing this transition process, the researchers were able to precisely calculate the density and characteristics of electronic traps.
The greatest advantage of this method is that multiple types of information can be obtained simultaneously from a single measurement. It allows not only the evaluation of how fast electrons move, how long they survive, and how far they travel, but also the properties of traps that interfere with electron transport.
The team first validated the accuracy of the technique using silicon semiconductors and then applied it to perovskites, which are attracting attention as next-generation solar cell materials. As a result, they successfully detected extremely small quantities of electronic traps that were difficult to identify using existing methods—demonstrating a sensitivity approximately 1,000 times higher than that of conventional techniques.
< Conceptual Diagram of the Evolution of Hall Characterization (Analysis) Techniques >
Professor Byungha Shin stated, “This study presents a new method that enables simultaneous analysis of electrical transport and the factors that hinder it within semiconductors using a single measurement,” adding that “it will serve as an important tool for improving the performance and reliability of various semiconductor devices, including memory semiconductors and solar cells.”
The results of this research were published on January 1 in Science Advances, an international academic journal, with Chaeyoun Kim, a doctoral student in the Department of Materials Science and Engineering, as the first author.
※ Paper title: “Electronic trap detection with carrier-resolved photo-Hall effect,” DOI: https://doi.org/10.1126/sciadv.adz0460
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
< Conceptual Diagram of Charge Transport and Trap Characterization Using Photo-Hall Measurements (AI-generated image) >
2026.01.08 View 366 -
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 343 -
Success in Measuring Nano Droplets, A New Breakthrough in Hydrogen, Semiconductor, and Battery Research
<(From Left) Ph.D candidate Uichang Jeong, Professor Seungbum Hong>
In hydrogen production catalysts, water droplets must detach easily from the surface to prevent blockage by bubbles, allowing for faster hydrogen generation. In semiconductor manufacturing, the quality of the process is determined by how evenly water or liquid spreads on the surface, or how quickly it dries. However, directly observing how such water or liquid spreads and moves on a surface ('wettability') at the nanoscale has been technically almost impossible until now, forcing researchers to rely mostly on conjecture. KAIST announced on December 2nd that a research team led by Professor Seungbum Hong of the Department of Materials Science and Engineering, in collaboration with Professor Jongwoo Lim's team at Seoul National University, has developed a technology to directly observe nano-sized water droplets in real-time using an Atomic Force Microscope (AFM) and to calculate the contact angle based on the droplet's shape. This research, by enabling the visual confirmation of the actual shape of nano-droplets, allows for the precise analysis of how well water droplets adhere to and detach from a surface. This is expected to be immediately applicable to various advanced technologies where liquid movement determines performance, such as hydrogen production catalysts, fuel cells, batteries, and semiconductor processes. Recently, precise measurement at the nanoscale has become crucial for wettability analysis technology. Traditional methods using large water droplets, several millimeters in size, could distinguish between hydrophilicity (where water spreads easily) and hydrophobicity (where water doesn't spread easily) on the surface. However, at the nanoscale, the droplets are too small to directly observe their shape. The research team successfully induced nano-droplets to form naturally by gently cooling the surface to a temperature where atmospheric water vapor does not freeze. They then observed these droplets using the non-contact mode of the AFM to capture their original shape. Since nano-droplets are sensitive and can be deformed by mere contact with the probe, precise control is essential. Furthermore, when the team applied this technique to the ferroelectric material lithium tantalate, they were the first to confirm a difference in the nano-droplet contact angle depending on the material's electrical direction (polarization). This difference, which was not visible with large droplets, demonstrates that nano-droplets are highly sensitive to the electrical state of the surface. The team then applied this technology to the water electrolysis catalyst used in hydrogen production, observing a single nano-droplet. This result aids in understanding how water reacts on the catalyst surface and can be used to analyze catalyst performance, particularly how well bubbles detach.
<Figure 1. Nanoscale droplet visualization using non-contact mode>
<Figure 2. Single-droplet visualization formed on sub-micron-sized water-splitting catalyst LiFeLDH particles>
Professor Seungbum Hong stated, "This research is an important case demonstrating that the Atomic Force Microscope can be used to directly visualize nano-sized water droplets and even measure the contact angle. Being able to observe the behavior of water droplets in the nano-world, which was previously invisible, will establish this as a core analysis technology for the development of next-generation energy and electronic materials." This research, in which Uichang Jeong, a PhD candidate in the KAIST Department of Materials Science and Engineering, participated as the first author, was published on October 17th in 'ACS Applied Materials and Interfaces', a prestigious journal in the field of new materials and chemical engineering published by the American Chemical Society (ACS).
Paper Title: Nanoscale Visualization and Contact Angle Analysis of Water Droplets on Ferroelectric Materials
DOI: https://doi.org/10.1021/acsami.5c14404
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.12.03 View 743 -
KAIST Develops an AI Semiconductor Brain Combining Transformer's Intelligence and Mamba's Efficiency
<(From Left) Ph.D candidate Seongryong Oh, Ph.D candidate Yoonsung Kim, Ph.D candidate Wonung Kim, Ph.D candidate Yubin Lee, M.S candidate Jiyong Jung, Professor Jongse Park, Professor Divya Mahajan, Professor Chang Hyun Park>
As recent Artificial Intelligence (AI) models’ capacity to understand and process long, complex sentences grows, the necessity for new semiconductor technologies that can simultaneously boost computation speed and memory efficiency is increasing. Amidst this, a joint research team featuring KAIST researchers and international collaborators has successfully developed a core AI semiconductor 'brain' technology based on a hybrid Transformer and Mamba structure, which was implemented for the first time in the world in a form capable of direct computation inside the memory, resulting in a four-fold increase in the inference speed of Large Language Models (LLMs) and a 2.2-fold reduction in power consumption.
KAIST (President Kwang Hyung Lee) announced on the 17th of October that the research team led by Professor Jongse Park from KAIST School of Computing, in collaboration with Georgia Institute of Technology in the United States and Uppsala University in Sweden, developed 'PIMBA,' a core technology based on 'AI Memory Semiconductor (PIM, Processing-in-Memory),' which acts as the brain for next-generation AI models.
Currently, LLMs such as ChatGPT, GPT-4, Claude, Gemini, and Llama operate based on the 'Transformer' brain structure, which sees all of the words simultaneously. Consequently, as the AI model grows and the processed sentences become longer, the computational load and memory requirements surge, leading to speed reductions and high energy consumption as major issues.
To overcome these problems with Transformer, the recently proposed sequential memory-based 'Mamba' structure introduced a method for processing information over time, increasing efficiency. However, memory bottlenecks and power consumption limits still remained.
Professor Park Jongse's research team designed 'PIMBA,' a new semiconductor structure that directly performs computations inside the memory in order to maximize the performance of the 'Transformer–Mamba Hybrid Model,' which combines the advantages of both Transformer and Mamba.
While existing GPU-based systems move data out of the memory to perform computations, PIMBA performs calculations directly within the storage device without moving the data. This minimizes data movement time and significantly reduces power consumption.
<Analysis of Post-Transformer Models and Proposal of a Problem-Solving Acceleration System>
As a result, PIMBA showed up to a 4.1-fold improvement in processing performance and an average 2.2-fold decrease in energy consumption compared to existing GPU systems.
The research outcome is scheduled to be presented on October 20th at the '58th International Symposium on Microarchitecture (MICRO 2025),' a globally renowned computer architecture conference that will be held in Seoul. It was previously recognized for its excellence by winning the Gold Prize at the '31st Samsung Humantech Paper Award.' ※Paper Title: Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving, DOI: 10.1145/3725843.3756121
This research was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP), the AI Semiconductor Graduate School Support Project, and the ICT R&D Program of the Ministry of Science and ICT and the IITP, with assistance from the Electronics and Telecommunications Research Institute (ETRI). The EDA tools were supported by IDEC (the IC Design Education Center).
2025.10.17 View 2168 -
Next Generation Robots Roaming Shipyards and City Centers
< Diden Robotics Research Team Co., Ltd (Leftmost person in the front row is CEO Joon-Ha Kim)>
KAIST announced on the September 30th that domestic robot startups, founded on KAIST research achievements, are driving new innovation at shipyards and urban worksites.
An industrial walking robot that freely climbs walls and ceilings and a humanoid walking robot that walks through downtown Gangnam are attracting attention as they enter the stage of commercialization. The stars are DIDEN Robotics Co., Ltd. and Eurobotics Co., Ltd.
Diden Robotics is providing a new breakthrough in the industrial automation market, including the shipbuilding industry, by commercializing its innovative 'Seungwol (Ascend and Cross) Robot' technology, which allows it to move freely and work on steel walls and ceilings. Eurobotics is commercializing world-class humanoid walking technology, and this achievement is scheduled to be officially presented at the international humanoid robot conference, 'Humanoids 2025,' to be held on October 1st.
< Diden Robot's Outer Plate (Longi) and Welding Test >
Diden Robotics is a robotics startup jointly founded in March 2024 by four alumni from the KAIST Mechanical Engineering Hu-bo Lab DRCD research team (Professor Hae-Won Park). Its flagship product, 'DIDEN 30,' is a quadrupedal robot designed for use in high-risk work environments that are difficult for humans to access, combining autonomous driving technology, a foot-shaped leg structure, and magnetic feet.
The 'DIDEN 30' successfully completed the 'Longitudinal (longi) Overcoming Test,' in which it stepped over steel stiffeners (longitudinals) densely installed as part of the structure at a ship construction site, proving its potential for field deployment. Currently, the company is conducting research to enhance its functionality so it can stably pass through access holes, the narrow entryways inside ships. It is also pushing for performance improvements so it can be deployed for real tasks such as welding, inspection, and painting starting in the second half of 2026.
A next-generation bipedal walking robot, 'DIDEN Walker,' is also under development. Targeting the completion of a prototype in the fourth quarter of 2025, it is being designed for stable walking in cramped and complex industrial environments. Plans are also underway to equip it with an upper-body manipulator for automated welding in the shipbuilding industry.
Diden Robotics is accelerating the advancement of its proprietary 'Physical AI' technology. The core is the self-developed AI learning platform, 'DIDEN World,' which applies an offline reinforcement learning method where the AI generates optimal motion data in a virtual simulation beforehand and learns without trial and error, increasing learning efficiency and stability.
< Diden Robot (DIDEN 30) >
Furthermore, to actually implement the AI technology, the company is internalizing its hardware and advancing its 3D recognition technology, which serves as the robot's 'eyes.' It is aiming for a completely autonomous walking system that requires no worker intervention by 2026, using technology such as 3D mapping based on four cameras.
In addition to this technological development, Diden Robotics successfully performed the longitudinal overcoming, Seungwol test, and welding work on blocks under construction through a joint development with Samsung Heavy Industries in September. This is a significant achievement, meaning Diden Robotics' technology has been validated in actual industrial settings, moving beyond the laboratory level.
Meanwhile, Diden Robotics is collaborating with major domestic shipyards, including Samsung Heavy Industries, HD Hyundai Samho, Hanwha Ocean, and HD Korea Shipbuilding & Offshore Engineering, to develop site-customized robots.
Joon-Ha Kim, CEO of Diden Robotics, stated, "The successful tests at the Samsung Heavy Industries site proved the practicality and stability of our technology. We will establish ourselves as a leading company in solving labor shortages and driving automation in the shipbuilding industry."
< (Eurobotics Research Team Co., Ltd.)(Leftmost person in the top row is CEO Byung-ho Yoo) >
Eurobotics is an autonomous walking startup jointly founded by three alumni from Professor Hyun Myung's research team at KAIST. It is promoting the commercialization of autonomous walking technology for indoor and outdoor industrial sites, including rough terrain. In a recently released video, a humanoid equipped with control technology developed by Eurobotics attracted attention by walking naturally through the crowd in downtown Gangnam.
The core technology is the 'Blind Walking Controller.' It determines locomotion based only on internal information without external sensors like cameras or LiDAR, enabling stable walking regardless of day, night, or weather. The robot performs locomotion by 'imagining' the terrain without precise terrain modeling, demonstrating robust performance with the same controller across various environments such as sidewalks, downhill slopes, and stairs.
This technology originated from the quadrupedal walking competition at the 2023 International Conference on Robotics and Automation (ICRA), where Professor Myung's lab participated, and proved its world-class capability by winning, beating MIT by a large margin. At the time, Byungg-ho Yoo, CEO of Eurobotics, led the team, and Co-CTOs Min-ho Oh and Dong-kyu Lee directly participated in developing the core autonomous walking technology. Based on this, they continued further development tailored to the humanoid environment and have entered the commercialization stage.
< Eurobotics' Humanoid Walking >
Byung-ho Yoo, CEO of Eurobotics, emphasized, "This video is the first step toward complete humanoid autonomous walking. We will develop KAIST's research achievements into technologies that can be immediately utilized in industrial settings."
Hyeonmin Bae, Head of the KAIST Startup Center, said, "We will provide close support from the initial stages to help the on-campus robotics industry grow actively and assist them in settling down stably."
Kwang Hyung Lee, President of KAIST, stated, "This achievement is a representative case showing that KAIST's fundamental technologies are rapidly spreading to industrial fields through startups. KAIST will continue to actively support innovative entrepreneurship based on challenging research and help lead the global robotics industry."
※ https://2025humanoids.org https://www.seoulairobot.com/
2025.09.30 View 3104 -
KAIST Develops Semiconductor Neuron that Remembers and Responds Like the Brain
<(From left, clockwise) Professor Kyung Min Kim, Min-Gu Lee, Dae-Hee Kim, Dr. Han-Chan Song, Tae-Uk Ko, Moon-Gu Choi, and Eun-Young Kim>
The human brain does more than simply regulate synapses that exchange signals; individual neurons also process information through “intrinsic plasticity,” the adaptive ability to become more sensitive or less sensitive depending on context. Existing artificial intelligence semiconductors, however, have struggled to mimic this flexibility of the brain. A KAIST research team has now developed next-generation, ultra-low-power semiconductor technology that implements this ability as well, drawing significant attention.
KAIST (President Kwang Hyung Lee) announced on September 28 that a research team led by Professor Kyung Min Kim of the Department of Materials Science and Engineering developed a “Frequency Switching Neuristor” that mimics “intrinsic plasticity,” a property that allows neurons to remember past activity and autonomously adjust their response characteristics.
“Intrinsic plasticity” refers to the brain’s adaptive ability- for example, becoming less startled when hearing the same sound repeatedly, or responding more quickly to a specific stimulus after repeated training. The “Frequency Switching Neuristor” is an artificial neuron device that autonomously adjusts the frequency of its signals, much like how the brain becomes less startled by repeated stimuli or, conversely, increasingly sensitive through training.
The research team combined a “volatile Mott memristor,” which reacts momentarily before returning to its original state, with a “non-volatile memristor,” which remembers input signals for long periods of time. This enabled the implementation of a device that can freely control how often a neuron fires (its spiking frequency).
<Figure 1. Conceptual comparison between a neuron and a frequency-tunable neuristor. The intrinsic plasticity of brain neurons regulates excitability through ion channels. Similarly, the frequency-tunable neuristor uses a volatile Mott device to generate current spikes, while a non-volatile VCM device adjusts resistance states to realize comparable frequency modulation characteristics>
In this device, neuronal spike signals and memristor resistance changes influence each other, automatically adjusting responses. Put simply, it reproduces within a single semiconductor device how the brain becomes less startled by repeated sounds or more sensitive to repeated stimuli.
To verify the effectiveness of this technology, the researchers conducted simulations with a “sparse neural network.” They found that, through the neuron’s built-in memory function, the system achieved the same performance with 27.7% less energy consumption compared to conventional neural networks.
They also demonstrated excellent resilience: even if some neurons were damaged, intrinsic plasticity allowed the network to reorganize itself and restore performance. In other words, artificial intelligence using this technology consumes less electricity while maintaining performance, and it can compensate for partial circuit failures to resume normal operation.
Professor Kyung Min Kim, who led the research, stated, “This study implemented intrinsic plasticity, a core function of the brain, in a single semiconductor device, thereby advancing the energy efficiency and stability of AI hardware to a new level. This technology, which enables devices to remember their own state and adapt or recover even from damage, can serve as a key component in systems requiring long-term stability, such as edge computing and autonomous driving.”
This research was carried out with Dr. Woojoon Park (now at Forschungszentrum Jülich, Germany) and Dr. Hanchan Song (now at ETRI) as co-first authors, and the results were published online on August 18 in Advanced Materials (IF 26.8), a leading international journal in materials science.
※ Paper title: “Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing,” DOI: 10.1002/adma.202502255
This research was supported by the National Research Foundation of Korea and Samsung Electronics.
2025.09.30 View 1559 -
KAIST Holds Opening Ceremony for Advanced Semiconductor Research Equipment
KAIST announced on the 8th of September that its Graduate School of Semiconductor Technology held an opening ceremony for advanced equipment at 3 p.m. on the 8th at the Department of Electrical Engineering (E3-2) in the main campus in Daejeon. The event unveiled state-of-the-art research infrastructure that can be utilized by industry, academia, and research institutions.
The event was attended by approximately 80 people, including KAIST President Kwang Hyung Lee, Daejeon Mayor Jang Woo Lee, and officials from the Ministry of Trade, Industry and Energy, the Korea Institute for Advancement of Technology, companies, and research institutions. The ceremony included a plaque of appreciation presented to Synopsys Korea, congratulatory speeches, and an introduction to the equipment. Attendees toured the newly established equipment and facilities, expressing high expectations for the development of local industries.
<Group photo of attendees at the opening ceremony of advanced equipment at the Graduate School of Semiconductor Technology>
The advanced equipment introduced this time is a key infrastructure for research in semiconductor devices, materials, and packaging. It provides a comprehensive research environment that covers the entire semiconductor development process, from design and simulation to fabrication and evaluation. It is expected to function as a practical hub for collaboration between industry, academia, and research institutions, as it will be open not only to KAIST professors and students but also to local companies and research organizations.
In particular, the Graduate School of Semiconductor Technology is a core institution that simultaneously promotes next-generation technology development and talent cultivation in the semiconductor sector, a national strategic industry. It serves as a hub for strengthening the competitiveness of the Korean semiconductor industry. Semiconductors, which are the foundation of all advanced industries, including artificial intelligence, batteries, autonomous driving, and defense, are in a field of fierce global supply chain competition. Therefore, establishing an educational and research hub where industry, academia, and research can closely cooperate is essential. The opening of this advanced equipment facility holds national significance, extending beyond simple research to support the establishment of a sustainable semiconductor ecosystem.
Daejeon City is actively supporting this project with an investment of 4.9 billion KRW. This reflects the city's commitment to consolidating its excellent research infrastructure and talent in the semiconductor industry to use it as a new growth engine for the local economy. The city's key strategy is to foster Daejeon into a practical hub for the Korean semiconductor industry through cooperation with KAIST.
<KAIST President Kwang Hyung Lee giving a welcoming speech at the opening ceremony of advanced equipment at the Graduate School of Semiconductor Technology>
KAIST also received a donation of a semiconductor process/device simulation software (TCAD) license from Synopsys Korea, a leading global semiconductor design software company, which provides it with world-class semiconductor education and research infrastructure.
The support project for the Graduate School of Semiconductor Technology is being pursued over a five-year period from 2023 to 2028, with a total budget of 21.5 billion KRW (15 billion KRW from the national government, 4.9 billion KRW from the city, and 1.6 billion KRW from KAIST's own funds). A faculty of 34 professors from the Department of Electrical Engineering, Materials Science and Engineering, Physics, Mechanical Engineering, and Chemical and Biomolecular Engineering plan to cultivate more than 225 highly skilled master's and doctoral level professionals. Currently, 123 students are enrolled in the graduate school, and it has achieved tangible results, such as carrying out collaborative projects with about 20 companies in an industry-academia consortium.
Daejeon Mayor Jang Woo Lee emphasized, "I hope that the combination of Daejeon's research infrastructure and talent will lead to the development of local industries. We will continue to strengthen cooperation with and actively support KAIST."
Gyeong-shin So, CEO of Synopsys Korea, stated, "I hope KAIST students will gain advanced simulation experience using TCAD and grow into key talents who will lead the global semiconductor industry."
<Photo of the tour of the Graduate School of Semiconductor Technology's equipment room>
President Kwang Hyung Lee said, "Daejeon is the optimal location for the semiconductor industry, equipped with the best research infrastructure and personnel in Korea. With the opening of this advanced equipment facility as an opportunity, KAIST will further contribute to strengthening national semiconductor competitiveness by creating innovative research results and fostering global talent."
This opening of the advanced equipment facility and the donation from Synopsys Korea are evaluated as important milestones that will lead to the qualitative growth of the Korean semiconductor industry. KAIST plans to cultivate global-level semiconductor talent and contribute to the development of the semiconductor industry on a national scale beyond Daejeon by developing new curricula and textbooks and promoting joint industry-academia projects in the future.
2025.09.09 View 1913 -
Semiconductor Leadership Spotlighted in Nature Sister Journal
<(From Left) Prof. Shinhyun Choi, Prof. Young Gyu Yoon, Prof.Seunghyub Yoo from the School of Electrical Engineering, Prof. Kyung Min Kim from Materials Science and Engineering>
KAIST (President Kwang Hyung Lee) announced on the 5th of September that its semiconductor research and education achievements were highlighted on August 18 in Nature Reviews Electrical Engineering, a sister journal of the world-renowned scientific journal Nature.
Title: Semiconductor-related research and education at KAIST DOI: 10.1038/s44287-025-00204-3
This special "Focus" article provides a detailed look at KAIST's leadership in next-generation semiconductor research, talent development, and global industry-academia collaboration, presenting a future blueprint for Korea's semiconductor industry. Editor Silvia Conti personally conducted the interviews, with KAIST professors including Kyung Min Kim from the Department of Materials Science and Engineering, and Young Gyu Yoon, Shinhyun Choi, Sung-Yool Choi, and Seunghyub Yoo from the School of Electrical Engineering, participating.
KAIST operates educational programs such as the School of Electrical Engineering, the Department of Semiconductor Systems Engineering, and the Graduate School of Semiconductor Engineering. It is leading next-generation semiconductor research in areas like neuromorphic computing, in-memory computing, and 2D new material-based devices. Building on this foundation, researchers are developing new architectures and devices that transcend the limitations of existing silicon, driving innovation in various application fields such as artificial intelligence, robotics, and medicine.
Notably, research on implementing biological functions like synapses and neurons into hardware platforms using new types of memory such as RRAM and PRAM is gaining international attention. This work opens up possibilities for applications in robots, edge computing, and on-sensor AI systems.
Furthermore, KAIST has operated EPSS (Samsung Advanced Human Resources Training Program) and KEPSI (SK Hynix Semiconductor Advanced Human Resources Training Program) based on long-standing partnerships with Samsung Electronics and SK Hynix. Graduate students in these programs receive full scholarships and are guaranteed employment after graduation. The Department of Semiconductor Systems Engineering, newly established in 2022, selects 100 undergraduate students each year to provide systematic education. Additionally, the KAIST–Samsung Electronics Industry-Academia Cooperation Center, which involves more than 70 labs annually, serves as a long-term hub for joint industry-academia research, contributing to solving critical issues within the industry.
The article emphasizes KAIST's growth beyond a simple research institution into an international research hub. KAIST is enhancing diversity and inclusivity by expanding the hiring of female faculty and establishing a Global Talent Visa Center to support foreign professors and students, attracting outstanding talent from around the world. As a core university within the Daedeok Research Complex (Daedeok Innopolis), it serves as the heart of "Korea's Silicon Valley."
KAIST researchers predict that the future of semiconductor technology is not in simple device miniaturization but in a convergent approach involving neuromorphic technology, 3D packaging technology, and AI applications. This article shows that KAIST's strategic research direction and leadership are gaining attention from both the global academic and industrial communities.
Professor Kyung Min Kim stated, "I am very pleased that KAIST's next-generation semiconductor research and talent development strategy has been widely publicized to domestic and international academia and industry through this article, and we will continue to contribute to the development of future semiconductor technology with innovative convergence research."
KAIST President Kwang Hyung Lee remarked, "Being highlighted for our semiconductor research and education achievements in a world-renowned science journal is a testament to the dedication and pioneering spirit of our university members. I am delighted that KAIST's growth as a global research hub is gaining recognition, and we will continue to expand industry-academia collaboration to lead next-generation semiconductor innovation and play a key role in helping Korea become a future semiconductor powerhouse."
2025.09.05 View 3272 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 3238 -
KAIST Turns an Unprecedented Idea into Reality: Quantum Computing with Magnets
What started as an idea under KAIST’s Global Singularity Research Project—"Can we build a quantum computer using magnets?"—has now become a scientific reality. A KAIST-led international research team has successfully demonstrated a core quantum computing technology using magnetic materials (ferromagnets) for the first time in the world.
KAIST (represented by President Kwang-Hyung Lee) announced on the 6th of May that a team led by Professor Kab-Jin Kim from the Department of Physics, in collaboration with the Argonne National Laboratory and the University of Illinois Urbana-Champaign (UIUC), has developed a “photon-magnon hybrid chip” and successfully implemented real-time, multi-pulse interference using magnetic materials—marking a global first.
< Photo 1. Dr. Moojune Song (left) and Professor Kab-Jin Kim (right) of KAIST Department of Physics >
In simple terms, the researchers developed a special chip that synchronizes light and internal magnetic vibrations (magnons), enabling the transmission of phase information between distant magnets. They succeeded in observing and controlling interference between multiple signals in real time. This marks the first experimental evidence that magnets can serve as key components in quantum computing, serving as a pivotal step toward magnet-based quantum platforms.
The N and S poles of a magnet stem from the spin of electrons inside atoms. When many atoms align, their collective spin vibrations create a quantum particle known as a “magnon.”
Magnons are especially promising because of their nonreciprocal nature—they can carry information in only one direction, which makes them suitable for quantum noise isolation in compact quantum chips. They can also couple with both light and microwaves, enabling the potential for long-distance quantum communication over tens of kilometers.
Moreover, using special materials like antiferromagnets could allow quantum computers to operate at terahertz (THz) frequencies, far surpassing today’s hardware limitations, and possibly enabling room-temperature quantum computing without the need for bulky cryogenic equipment.
To build such a system, however, one must be able to transmit, measure, and control the phase information of magnons—the starting point and propagation of their waveforms—in real time. This had not been achieved until now.
< Figure 1. Superconducting Circuit-Based Magnon-Photon Hybrid System. (a) Schematic diagram of the device. A NbN superconducting resonator circuit fabricated on a silicon substrate is coupled with spherical YIG magnets (250 μm diameter), and magnons are generated and measured in real-time via a vertical antenna. (b) Photograph of the actual device. The distance between the two YIG spheres is 12 mm, a distance at which they cannot influence each other without the superconducting circuit. >
Professor Kim’s team used two tiny magnetic spheres made of Yttrium Iron Garnet (YIG) placed 12 mm apart with a superconducting resonator in between—similar to those used in quantum processors by Google and IBM. They input pulses into one magnet and successfully observed lossless transmission of magnon vibrations to the second magnet via the superconducting circuit.
They confirmed that from single nanosecond pulses to four microwave pulses, the magnon vibrations maintained their phase information and demonstrated predictable constructive or destructive interference in real time—known as coherent interference.
By adjusting the pulse frequencies and their intervals, the researchers could also freely control the interference patterns of magnons, effectively showing for the first time that electrical signals can be used to manipulate magnonic quantum states.
This work demonstrated that quantum gate operations using multiple pulses—a fundamental technique in quantum information processing—can be implemented using a hybrid system of magnetic materials and superconducting circuits. This opens the door for the practical use of magnet-based quantum devices.
< Figure 2. Experimental Data. (a) Measurement results of magnon-magnon band anticrossing via continuous wave measurement, showing the formation of a strong coupling hybrid system. (b) Magnon pulse exchange oscillation phenomenon between YIG spheres upon single pulse application. It can be seen that magnon information is coherently transmitted at regular time intervals through the superconducting circuit. (c,d) Magnon interference phenomenon upon dual pulse application. The magnon information state can be arbitrarily controlled by adjusting the time interval and carrier frequency between pulses. >
Professor Kab-Jin Kim stated, “This project began with a bold, even unconventional idea proposed to the Global Singularity Research Program: ‘What if we could build a quantum computer with magnets?’ The journey has been fascinating, and this study not only opens a new field of quantum spintronics, but also marks a turning point in developing high-efficiency quantum information processing devices.”
The research was co-led by postdoctoral researcher Moojune Song (KAIST), Dr. Yi Li and Dr. Valentine Novosad from Argonne National Lab, and Prof. Axel Hoffmann’s team at UIUC. The results were published in Nature Communications on April 17 and npj Spintronics on April 1, 2025.
Paper 1: Single-shot magnon interference in a magnon-superconducting-resonator hybrid circuit, Nat. Commun. 16, 3649 (2025)
DOI: https://doi.org/10.1038/s41467-025-58482-2
Paper 2: Single-shot electrical detection of short-wavelength magnon pulse transmission in a magnonic ultra-thin-film waveguide, npj Spintronics 3, 12 (2025)
DOI: https://doi.org/10.1038/s44306-025-00072-5
The research was supported by KAIST’s Global Singularity Research Initiative, the National Research Foundation of Korea (including the Mid-Career Researcher, Leading Research Center, and Quantum Information Science Human Resource Development programs), and the U.S. Department of Energy.
2025.06.12 View 9396 -
KAIST Develops Neuromorphic Semiconductor Chip that Learns and Corrects Itself
< Photo. The research team of the School of Electrical Engineering posed by the newly deveoped processor. (From center to the right) Professor Young-Gyu Yoon, Integrated Master's and Doctoral Program Students Seungjae Han and Hakcheon Jeong and Professor Shinhyun Choi >
- Professor Shinhyun Choi and Professor Young-Gyu Yoon’s Joint Research Team from the School of Electrical Engineering developed a computing chip that can learn, correct errors, and process AI tasks
- Equipping a computing chip with high-reliability memristor devices with self-error correction functions for real-time learning and image processing
Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.
KAIST (President Kwang Hyung Lee) announced on the 17th of January that the joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own.
< Figure 1. Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32×32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). >
What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.
This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team's main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components.
The research team has developed the world's first memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology.
< Figure 2. Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by our research team (right). >
At the heart of this innovation is a next-generation semiconductor device called a memristor*. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilizing it, data storage and computation can be performed simultaneously, just like our brain cells.
*Memristor: A compound word of memory and resistor, next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past.
The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialization possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference.
This technology will revolutionize the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected, and more energy-efficient.
“This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” explained KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.”
The research was conducted with Hakcheon Jeong and Seungjae Han, the students of Integrated Master's and Doctoral Program at KAIST School of Electrical Engineering being the co-first authors, the results of which was published online in the international academic journal, Nature Electronics, on January 8, 2025.
*Paper title: Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array ( https://doi.org/10.1038/s41928-024-01318-6 )
This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Project, Excellent New Researcher Project and PIM AI Semiconductor Core Technology Development Project of the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute Research and Development Support Project of the Institute of Information & communications Technology Planning & Evaluation.
2025.01.17 View 14796