With participation from Professor Insu Yun’s research team at KAIST’s School of Electrical Engineering, Samsung Research, POSTECH, and Georgia Institute of Technology formed “Team Atlanta” and won first place in the AI Cyber Challenge (AIxCC) hosted by the U.S. Defense Advanced Research Projects Agency (DARPA) at the world’s largest hacking conference, DEF CON 33, held in Las Vegas on August 8 (local time).
Led by Taesoo Kim of Samsung Research and Georgia Institute of Technology, Team Atlanta earned USD 4 million (approx. KRW 5.5 billion) in prize money, proving the excellence of AI-based autonomous cyber defense technology on the global stage.
<(from left, second)Taesoo Kim, Samsung Research and Georgia Institute of Technology; Hyungseok Han, Samsung Research America; Professor Insu Yun>
The AI Cyber Challenge (AIxCC) is a two-year global competition jointly organized by DARPA and the U.S. Advanced Research Projects Agency for Health (ARPA-H). It challenges teams to use AI-based Cyber Reasoning Systems (CRS) to automatically analyze, detect, and fix software vulnerabilities. The total prize pool is USD 29.5 million, with USD 4 million awarded to the final winner.
In the final round, Team Atlanta scored 392.76 points, beating second-place Trail of Bits by more than 170 points to secure a decisive victory.
The Cyber Reasoning System (CRS) developed by Team Atlanta successfully detected various types of vulnerabilities and patched many of them in real time during the competition. Among the 70 artificially injected vulnerabilities in the final, the seven finalist teams detected an average of 77% and patched 61% of them. In addition, they discovered 18 previously unknown vulnerabilities in real-world software, demonstrating the potential of AI security technology.
<Final results scoreboard – overwhelming victory by a margin of over 170 points>
All CRS technologies, including that of the winning team, will be made open source and are expected to be used to strengthen the security of critical infrastructure such as hospitals, water systems, and power grids.
Professor Insu Yun said, “I am very pleased with this tremendous achievement. This victory demonstrates that Korea’s cybersecurity research has reached the highest global standards, and it was meaningful to showcase the capabilities of Korean researchers on the world stage. We will continue research that combines AI and security technologies to safeguard the digital safety of both our nation and the global community.”
KAIST President Kwang Hyung Lee stated, “This victory is another proof that KAIST is a global leader in the convergence of future cybersecurity and artificial intelligence. We will continue to provide full support so that our researchers can compete confidently on the world stage and achieve outstanding results.”
For the first time world, a Korean research team has devised and experimentally validated a “Measurement‐protection (MP)” theory that enables stable quantum key distribution (QKD) without any measurement calibration.
Professor Joonwoo Bae’s team from our School, in collaboration with the Quantum Communications Laboratory at the Electronics and Telecommunications Research Institute (ETRI), has developed a new technology that enables stable quantum communication in moving environments such as satellites, ships, and drones.
Quantum communication is a high-precision technology that transmits information via the quantum states of light, but in wireless, moving environments it has suffered from severe instability due to weather and surrounding environmental changes. In particular, in rapidly changing settings like the sky, sea, or air, reliably delivering quantum states has been extremely challenging.
This research is significant as it overcomes those limitations and opens up possibility of exchanging quantum information stably even while in motion. It is expected that quantum technology can be applied in the future to secure communications between satellites and ground stations, as well as to drone and maritime communications.
Quantum key distribution (QKD) is a technology that uses the principles of quantum mechanics to distribute cryptographic keys that are fundamentally immune to eavesdropping. Existing QKD protocols required repeated recalibration of the receiver’s measurement devices whenever the channel conditions changed.
However, in this work the team proved that, with only simple local operations, stable key distribution is possible regardless of channel conditions. The theory was developed by Professor Bae’s group, and the experiments were carried out by ETRI researchers.
To generate single-photon pulses, the researchers used a 100 MHz light source: a vertical-cavity surface-emitting laser (VCSEL). A VCSEL is a type of semiconductor laser whose beam is emitted vertically from the top surface of the chip.
They emulated a long-distance free-space link with up to 30 dB loss over a 10 m path and inserted various polarization noise to simulate a wireless environment. Even under these harsh conditions, they confirmed that quantum transmission and measurement remained reliable. Both the transmitter and receiver were equipped with three waveplates each to implement the required local operations.
As a result, they demonstrated that an MP-based QKD system can raise the system’s maximum tolerable quantum bit error rate (QBER), the fraction of transmitted qubits received in error, by up to 20.7% compared to conventional approaches.
In other words, if the received QBER is below 20.7%, stable quantum key distribution is possible without any measurement calibration. This establishes the foundation for implementing reliable quantum communication across a variety of noisy channel environments. The team believes this achievement can be applied to scenarios similar to satellite-ground links.
The study was published on June 25 in the IEEE’s prestigious communications journal, “Journal on Selected Areas in Communications”, with ETRI’s Heasin Ko and KAIST’s Spiros Kechrimparis serving as co-first authors.
Professor Bae commented, “This result will be a decisive turning point in bringing reliable quantum-secure communication into practical reality, even under complex environments.”
This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP) through the “Core Technology Development for Quantum Internet,” “ETRI R&D Support Project,” “Quantum Cryptography Communication Industry Expansion and Next-Generation Technology Development Project,” “Quantum Cryptography Communication Integration and Transmission Technology Advancement Project,” and “SW Computing Industrial Core Technology Development Project”; by the National Research Foundation of Korea through the “Quantum Common-Base Technology Development Project” and “Mid-Career Researcher Program”; and as part of the Future Space Education Center initiative of the Korea Aerospace Agency.
<(From left) Ph.D candidate Youngmin Sim, Ph.D candidate Do Yun Park, Dr. Chanho Park, Professor Kyeongha Kwon>
Miniaturization and weight reduction of medical wearable devices for continuous health monitoring such as heart rate, blood oxygen saturation, and sweat component analysis remain major challenges. In particular, optical sensors consume a significant amount of power for LED operation and wireless transmission, requiring heavy and bulky batteries. To overcome these limitations, KAIST EE researchers have developed a next-generation wearable platform that enables 24-hour continuous measurement by using ambient light as an energy source and optimizing power management according to the power environment.
Professor Kyeongha Kwon’s team from the School of Electrical Engineering, in collaboration with Dr. Chanho Park’s team at Northwestern University in the U.S., has developed an adaptive wireless wearable platform that reduces battery load by utilizing ambient light.
To address the battery issue of medical wearable devices, Professor Kyeongha Kwon’s research team developed an innovative platform that utilizes ambient natural light as an energy source. This platform integrates three complementary light energy technologies.
<Figure1.The wireless wearable platform minimizes the energy required for light sources through i) Photometric system that directly utilizes ambient light passing through windows for measurements, ii) Photovoltaic system that receives power from high-efficiency photovoltaic cells and wireless power receiver coils, and iii) Photoluminescent system that stores light using photoluminescent materials and emits light in dark conditions to support the two aforementioned systems. In-sensor computing minimizes power consumption by wirelessly transmitting only essential data. The adaptive power management system efficiently manages power by automatically selecting the optimal mode among 11 different power modes through a power selector based on the power supply level from the photovoltaic system and battery charge status.>
The first core technology, the Photometric Method, is a technique that adaptively adjusts LED brightness depending on the intensity of the ambient light source. By combining ambient natural light with LED light to maintain a constant total illumination level, it automatically dims the LED when natural light is strong and brightens it when natural light is weak.
Whereas conventional sensors had to keep the LED on at a fixed brightness regardless of the environment, this technology optimizes LED power in real time according to the surrounding environment. Experimental results showed that it reduced power consumption by as much as 86.22% under sufficient lighting conditions.
The second is the Photovoltaic Method using high-efficiency multijunction solar cells. This goes beyond simple solar power generation to convert light in both indoor and outdoor environments into electricity. In particular, the adaptive power management system automatically switches among 11 different power configurations based on ambient conditions and battery status to achieve optimal energy efficiency.
The third innovative technology is the Photoluminescent Method. By mixing strontium aluminate microparticles* into the sensor’s silicone encapsulation structure, light from the surroundings is absorbed and stored during the day and slowly released in the dark. As a result, after being exposed to 500W/m² of sunlight for 10 minutes, continuous measurement is possible for 2.5 minutes even in complete darkness. *Strontium aluminate microparticles: A photoluminescent material used in glow-in-the-dark paint or safety signs, which absorbs light and emits it in the dark for an extended time.
These three technologies work complementarily—during bright conditions, the first and second methods are active, and in dark conditions, the third method provides additional support—enabling 24-hour continuous operation.
The research team applied this platform to various medical sensors to verify its practicality. The photoplethysmography sensor monitors heart rate and blood oxygen saturation in real time, allowing early detection of cardiovascular diseases. The blue light dosimeter accurately measures blue light, which causes skin aging and damage, and provides personalized skin protection guidance. The sweat analysis sensor uses microfluidic technology to simultaneously analyze salt, glucose, and pH in sweat, enabling real-time detection of dehydration and electrolyte imbalances.
Additionally, introducing in-sensor data computing significantly reduced wireless communication power consumption. Previously, all raw data had to be transmitted externally, but now only the necessary results are calculated and transmitted within the sensor, reducing data transmission requirements from 400B/s to 4B/s—a 100-fold decrease.
To validate performance, the research tested the device on healthy adult subjects in four different environments: bright indoor lighting, dim lighting, infrared lighting, and complete darkness. The results showed measurement accuracy equivalent to that of commercial medical devices in all conditions A mouse model experiment confirmed accurate blood oxygen saturation measurement in hypoxic conditions.
<Figure2.The multimodal device applying the energy harvesting and power management platform consists of i) photoplethysmography (PPG) sensor, ii) blue light dosimeter, iii) photoluminescent microfluidic channel for sweat analysis and biomarker sensors (chloride ion, glucose, and pH), and iv) temperature sensor. This device was implemented with flexible printed circuit board (fPCB) to enable attachment to the skin. A silicon substrate with a window that allows ambient light and measurement light to pass through, along with photoluminescent encapsulation layer, encapsulates the PPG, blue light dosimeter, and temperature sensors, while the photoluminescent microfluidic channel is attached below the photoluminescent encapsulation layer to collect sweat>
Professor Kyeongha Kwon of KAIST, who led the research, stated, “This technology will enable 24-hour continuous health monitoring, shifting the medical paradigm from treatment-centered to prevention-centered shifting the medical paradigm from treatment-centered to prevention-centered,” further stating that “cost savings through early diagnosis as well as strengthened technological competitiveness in the next-generation wearable healthcare market are anticipated.”
This research was published on July 1 in the international journal Nature Communications, with Do Yun Park, a doctoral student in the AI Semiconductor Graduate Program, as co–first author. ※ Paper title: Adaptive Electronics for Photovoltaic, Photoluminescent and Photometric Methods in Power Harvesting for Wireless and Wearable Sensors ※ DOI:https://doi.org/10.1038/s41467-025-60911-1 ※ URL:https://www.nature.com/articles/s41467-025-60911-1
This research was supported by the National Research Foundation of Korea (Outstanding Young Researcher Program and Regional Innovation Leading Research Center Project), the Ministry of Science and ICT and Institute of Information & Communications Technology Planning & Evaluation (IITP) AI Semiconductor Graduate Program, and the BK FOUR Program (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE).
< (From left) PhD candidate Luu Minh Tung, MS student Younghwan Lee, , MS student Donghoon Lee and Professor Chang D. Yoo >
With recent advancements in artificial intelligence’s ability to understand both language and visual information, there is growing interest in Physical AI, AI systems that can comprehend high-level human instructions and perform physical tasks such as object manipulation or navigation in the real world. Physical AI integrates large language models (LLMs), vision-language models (VLMs), reinforcement learning (RL), and robot control technologies, and is expected to become a cornerstone of next-generation intelligent robotics.
To advance research in Physical AI, an EE research team led by Professor Chang D. Yoo (U-AIM: Artificial Intelligence & Machine Learning Lab) has developed two novel reinforcement learning frameworks leveraging large vision-language models. The first, introduced in ICML 2025, is titled ERL-VLM (Enhancing Rating-based Learning to Effectively Leverage Feedback from Vision-Language Models). In this framework, a VLM provides absolute rating-based feedback on robot behavior, which is used to train a reward function. That reward is then used to learn a robot control AI model. This method removes the need for manually crafting complex reward functions and enables the efficient collection of large-scale feedback, significantly reducing the time and cost required for training.
<Figure 1. ERL-VLM framework>
The second, published in IROS 2025, is titled PLARE (Preference-based Learning from Vision-Language Model without Reward Estimation). Unlike previous approaches, PLARE skips reward modeling entirely and instead uses pairwise preference feedback from a VLM to directly train the robot control AI model. This makes the training process simpler and more computationally efficient, without compromising performance.
<Figure 2. PLARE framework>
Both frameworks demonstrated superior performance not only in simulation environments but also in real-world experiments using physical robots, achieving higher success rates and more stable behavior than existing methods—thereby verifying their practical applicability.
<Figure 4. (From left) PLARE experimental results (success Rate) and example of real-world robot experiment setup>
This research provides a more efficient and practical approach to enabling robots to understand and act upon human language instructions by leveraging large vision-language models—bringing us a step closer to the realization of Physical AI. Moving forward, Professor Changdong Yoo’s team plans to continue advancing research in robot control, vision-language-based interaction, and scalable feedback learning to further develop key technologies in Physical AI.
<(From left) Professor Yongdae Kim, PhD candidate Tuan Dinh Hoang, PhD candidate Taekkyung Oh from KAIST, Professor CheolJun Park from Kyung Hee University; and Professor Insu Yun from KAIST>
Smartphones must stay connected to mobile networks at all times to function properly. The corecomponent that enables this constant connectivity is the communication modem (Baseband) inside the device. KAIST researchers, using their self-developed testing framework called ‘LLFuzz (Lower Layer Fuzz),’ have discovered security vulnerabilities in the lower layers of smartphone communication modems and demonstrated the necessity of standardizing ‘mobile communication modem security testing.’ *Standardization: In mobile communication, conformance testing, which verifies normal operation in normal situations, has been standardized. However, standards for handling abnormal packets have not yet been established, hence the need for standardized security testing.
Professor Yongdae Kim’s team from the School of Electrical Engineering at KAIST, in collaboration with Professor CheolJun Park’s team from Kyung Hee University, has discovered critical security vulnerabilities in the lower layers of smartphone communication modems. These vulnerabilities can incapacitate smartphone communication with just a single manipulated wireless packet (a data transmission unit in a network). In particular, they are extremely severe, as they can potentially lead to remote code execution (RCE).
The research team utilized their self-developed ‘LLFuzz’ analysis framework to analyze the lower layer state transitions and error handling logic of the modem to detect security vulnerabilities. LLFuzz was able to precisely extract vulnerabilities caused by implementation errors by comparing and analyzing 3GPP* standard-based state machines with actual device responses. *3GPP: An international collaborative organization that creates global mobile communication standards.
The research team conducted experiments on 15 commercial smartphones from global manufacturers, including Apple, Samsung Electronics, Google, and Xiaomi, and discovered a total of 11 vulnerabilities. Among these, seven were assigned official CVE (Common Vulnerabilities and Exposures) numbers, and manufacturers applied security patches for these vulnerabilities. However, the remaining four have not yet been publicly disclosed.
While previous security research primarily focused on higher layers of mobile communication, such as NAS (Network Access Stratum) and RRC (Radio Resource Control), the research team concentrated on analyzing the error handling logic of mobile communication’s lower layers, which manufacturers have often neglected
<LLFuzz Design>
These vulnerabilities occurred in the lower layers of the communication modem (RLC, MAC, PDCP, PHY*), and due to their structural characteristics where encryption or authentication is not applied, operational errors could be induced simply by injecting external signals. *RLC, MAC, PDCP, PHY: Lower layers of LTE/5G communication, responsible for wireless resource allocation, error control, encryption, and physical layer transmission.
The research team released a demo video showing that when they injected a manipulated wireless packet (malformed MAC packet) into commercial smartphones via a Software-Defined Radio (SDR) device using packets generated on an experimental laptop, the smartphone’s communication modem (Baseband) immediately crashed
The video shows data being normally transmitted at 23MB per second on the fast.com page, but immediately after the manipulated packet is injected, the transmission stops and the mobile communication signal disappears. This intuitively demonstrates that a single wireless packet can cripple a commercial device’s communication modem.
<LTE Vulnerability Summary>
The vulnerabilities were found in the ‘modem chip,’ a core component of smartphones responsible for calls, texts, and data communication, making it a very important component.
Qualcomm: Affects over 90 chipsets, including CVE-2025-21477, CVE-2024-23385.
MediaTek: Affects over 80 chipsets, including CVE-2024-20076, CVE-2024-20077, CVE-2025-20659.
Samsung: CVE-2025-26780 (targets the latest chipsets like Exynos 2400, 5400).
Apple: CVE-2024-27870 (shares the same vulnerability as Qualcomm CVE).
The problematic modem chips (communication components) are not only in premium smartphones but also in low-end smartphones, tablets, smartwatches, and IoT devices, leading to the widespread potential for user harm due to their broad diffusion.
Furthermore, the research team experimentally tested 5G vulnerabilities in the lower layers and found two vulnerabilities in just two weeks. Considering that 5G vulnerability checks have not been generally conducted, it is possible that many more vulnerabilities exist in the mobile communication lower layers of baseband chips.
Professor Yongdae Kim explained, “The lower layers of smartphone communication modems are not subject to encryption or authentication, creating a structural risk where devices can accept arbitrary signals from external sources.” He added, “This research demonstrates the necessity of standardizing mobile communication modem security testing for smartphones and other IoT devices.”
The research team is continuing additional analysis of the 5G lower layers using LLFuzz and is also developing tools for testing LTE and 5G upper layers. They are also pursuing collaborations for future tool disclosure. The team’s stance is that “as technological complexity increases, systemic security inspection systems must evolve in parallel.”
First author Tuan Dinh Hoang, a Ph.D. student in the School of Electrical Engineering, will present the research results in August at USENIX Security 2025, one of the world’s most prestigious conferences in cybersecurity.
※ Paper Title: LLFuzz: An Over-the-Air Dynamic Testing Framework for Cellular Baseband Lower Layers (Tuan Dinh Hoang and Taekkyung Oh, KAIST; CheolJun Park, Kyung Hee Univ.; Insu Yun and Yongdae Kim, KAIST)
This research was conducted with support from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Ministry of Science and ICT.
〈(from left) Myotaeg Lim, President of ICROS and Eungchang Mason Lee, Ph.D. candidate〉
Eungchang Mason Lee, a Ph.D. candidate in Professor Hyun Myung’s lab in our School, has achieved the honor of receiving the Best Paper Award presented by KFMES (The Korea Federation of Mechanical Engineering Societies; 한국기계기술단체총연합회) at the 2025 Conference of Institute of Control, Robotics and Systems (ICROS).
Out of a total of 554 papers presented at the conference, 9 papers were selected for the Excellent Paper Award and 8 for the Best Paper Award. Among them, only one paper was honored with the KFMES Best Paper Award.
The award-winning paper, titled <Degeneracy-Robust LiDAR-Inertial Odometry with Adaptive Schmidt-Kalman Filter>, proposes a novel method for accurately and reliably estimating the pose of robot in extreme environments where LiDAR measurements are sparse or imbalanced.
<(From left) Uikyeong Lee (Master’s student), Byeongho Hwang (Doctoral student), Jihan Shin (Master’s student), and Jinho Park (Master’s student)>
Students from Professor Kwon Kyeongha’s laboratory in our School – Lee Uikyeong(master’s student), Hwang Byeongho(doctoral student), Shin Jihan, and Park Jinho(master’s students) – won the Grand Prize at the Semiconductor Live Demonstration Competition held during the Korean Society of Semiconductor & Display Technology Summer Conference.
The winning team developed an impedance measurement device for battery health diagnosis and conducted real-time demonstrations on-site. This system presented an innovative solution that enables precise evaluation of automotive battery performance and lifespan through high-precision impedance measurement.
The achievement is particularly significant for presenting a practical solution in the field of energy management, which is crucial in the electric vehicle era. Through this award, the research team’s hardware development capabilities have been recognized, and their work is expected to contribute to technological advancement in related fields.
< (From left) Young Min Song, Professor, KAIST School of Electrical Engineering and Hyeon‑Ho Jeong, Professor, GIST School of EECS >
Our department’s Professor Young Min Song, in collaboration with Professor Hyeon‑Ho Jeong’s research team at GIST School of EECS, has developed a replication‑impossible security authentication technology based on nature‑inspired nanophotonic structures.
This technology can be easily embedded into physical products such as ID cards or QR codes and, being visually indistinguishable from existing items, provides strong tamper‑proof protection without compromising design. It holds broad potential for applications requiring genuine‑product authentication, including premium consumer goods, pharmaceuticals, and electronics.
Until now, anti‑tampering measures like QR codes and barcodes have been limited by their ease of replication and the difficulty of assigning truly unique identifiers to each item. A recently spotlighted solution is the physically unclonable function (PUF)*, which leverages the natural randomness arising during manufacturing to grant each device a unique physical signature, thereby enhancing security and authentication reliability.
However, existing PUF technologies, while achieving randomness and uniqueness, have struggled with color consistency control and are easily identified (and thus attacked) from the outside. * Physically Unclonable Function (PUF): A technique that uses physical variations formed during the manufacturing process to generate a unique authentication key. Because these variations are inherently random and unclonable, even if the authentication data is stolen, constructing the exact hardware for authentication is effectively impossible.
In response, the research team turned its attention to the unique phenomenon of structural color* observed in natural organisms. For example, the wings of butterflies, feathers of birds, and leaves of seaweed all contain nanoscale microstructures arranged in a form of quasiorder*—a pattern that is neither completely ordered nor entirely random. These structures appear to exhibit uniform coloration to the naked eye, but internally contain subtle randomness that enables survival functions such as camouflage, communication, and predator evasion.
* Quasi‑order: A structural arrangement that is neither fully ordered nor fully disordered. In nature, nano‑scale elements are arranged in a pattern that blends order with randomness—found, for example, in butterfly wings, seaweed leaves, and bird feathers—producing uniform color at a macroscopic scale while embedding unique optical features.
* Structural Color: Color produced not by pigments but by nano‑meter‑scale structures that interact with light, commonly seen in living organisms. Classic examples include the iridescent wings of butterflies and the feathers of peacocks.
The researchers drew inspiration from these natural phenomena. They deposited a thin dielectric layer of HfO₂ onto a metallic mirror and then used electrostatic self‑assembly to arrange gold nanoparticles (tens of nanometers in size) into a quasi‑ordered plasmonic metasurface*. Visually, this nanostructure exhibits a uniform reflection color; under a high‑magnification optical microscope, however, each region reveals a distinct random scattering pattern—an “optical fingerprint*”—that is impossible to replicate. * Plasmonic Metasurface: An ultrathin optical structure comprising precisely arranged metallic nano‑elements that exploit surface plasmon resonance to locally enhance electromagnetic fields, enabling far more compact and precise light–matter interaction than conventional optics.* Optical Fingerprint: A unique pattern of reflection, scattering, and interference produced when light interacts with a micro‑ or nano‑scale structure. Because these patterns arise from random structural variations that cannot be exactly duplicated, they serve as a practically unclonable security feature.
The team confirmed that leveraging these nano‑scale stochastic patterns enhances PUF performance compared to conventional approaches.
In a hypothetical hacking scenario where an attacker attempts to recreate the device, the time required to decrypt the optical fingerprint would exceed the age of the Earth, rendering replication virtually impossible. Through demonstration experiments on pharmaceuticals, semiconductors, and QR codes, the researchers validated the technology’s practical industrial applicability.
Analysis of over 500 generated PUF keys showed an average bit‑value distribution of 0.501, which is remarkably close to the ideal balance of 0.5, and an average inter‑key Hamming distance of 0.494, demonstrating high uniqueness and reliability. Additionally, the scattering patterns remained stable under various environmental stresses, including high temperature, high humidity, and friction, confirming excellent durability.
Professor Young Min Song emphasized, “Whereas conventional security labels can be deformed by even minor damage, our technology secures both structural stability and unclonability. In particular, by separating visible color information from the invisible unique‑key information, it offers a new paradigm in security authentication.”
Professor Hyeon‑Ho Jeong added, “By reproducing structures in which order and disorder coexist in nature through nanotechnology, we have created optical information that appears identical externally yet is fundamentally unclonable. This technology can serve as a powerful anti‑counterfeiting measure across diverse fields, from premium consumer goods to pharmaceutical authentication and even national security.”
This work, guided by Professor Young Min Song (KAIST School of Electrical Engineering) and Professor Hyeon‑Ho Jeong (GIST School of EECS), and carried out by Gyurin Kim, Doeun Kim, JuHyeong Lee, Juhwan Kim, and Se‑Yeon Heo, was supported by the Ministry of Science and ICT and the National Research Foundation’s Early‑Career Research Program, the Regional Innovation Mega Project in R&D Special Zones, and the GIST‑MIT AI International Collaboration Project.
The results were published online on July 8, 2025, in the international journal Nature Communications.
* Paper title: Quasi‑ordered plasmonic metasurfaces with unclonable stochastic scattering for secure authentication
< (from left) M.S.& Ph.D. integrated candidate Dae Hyun Kang , Seung Hoon Kim >
Students Dae Hyun Kang and Seung Hoon Kim (M.S.& Ph.D. integrated program) from Professor Byung Jin Cho’s Research Lab have been honored with the Best Oral Presentation Award at the 2025 Summer Conference of the Korean Institute of Electrical and Electronic Material Engineers (KIEEME).
The KIEEME Summer Conference is one of the most prestigious academic events in Korea for the fields of electronic materials and semiconductors. It serves as a key venue for sharing the latest research achievements, discussing industrial trends, and promoting academic–industrial collaboration.
Dae Hyun Kang presented a paper titled “Performance Enhancement of Charge Trap Flash Memory via Silicon-Doped Boron Nitride Energy Barrier,” while Sheung Hun Kim presented “Analysis of Disturbance Behavior through Lanthanum Interface Treatment in Hafnium Oxide Ferroelectric-Based FeFET Memory.”
Both presentations received high evaluations for originality, technical completeness, and contributions to both academia and industry, leading them to be jointly awarded the Best Oral Presentation Award.
This achievement is particularly significant as it highlights the breakthrough potential of advanced charge trap memory and FeFET memory technologies in overcoming performance limitations and improving device reliability, gaining strong recognition from both academia and industry.
< (third from the left) Ph.D. candidate Carmela Michelle Esteban>
Carmela Michelle Esteban, a Ph.D. candidate in the research group of Professor Seunghyup Yoo at KAIST School of Electrical Engineering, received the Young Researcher Award for Best Poster Presentation at the 18th International Symposium on Flexible Organic Electronics (ISFOE25), held from July 7 to 10 in Thessaloniki, Greece.
Michelle was recognized for her outstanding research presentation titled “Multi-Functional Polymeric Substrate with Integrated Optical Layers for Flexible Organic Photodetectors.”
ISFOE is a prestigious international symposium in the field of flexible organic and printed electronics, held annually to foster innovation in next-generation electronics. Each year, the Young Researcher Awards are presented to graduate students who demonstrate academic excellence and exceptional research achievements in the field.
Awardees receive a certificate and a complimentary publication for use in the Nanomaterials journal published by MDPI.