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EE Professor Young-Gyu Yoon Selected as a Recipient of the Human Frontier Science Program (HFSP) Award

〈 Professor Young-Gyu Yoon 〉

Professor Young-Gyu Yoon has been selected as a recipient of the 2025 Human Frontier Science Program (HFSP) Award.

Beginning in 1990, the HFSP Organization has annually awarded outstanding researchers. This year, for the first time, HFSP introduced a new “Accelerator Track,” and Professor Yoon was honored as the recipient of this award. He was recognized for his pioneering international collaborative research and leadership in the field of optical brain functional imaging and analysis.

Over the next two years, Professor Yoon will receive approximately USD 100,000 annually in research support. He will collaborate with Professor Botero of the University of Texas at Austin, Professor Culver of the University of Washington, and Professor Guntürkün of Ruhr University Bochum in Germany. Their joint research will focus on investigating how environmental and evolutionary factors influence the nervous system.

Professor Yoon stated, “As an electronic engineer studying neuroscience technology, I am honored to receive the HFSP award, which is typically awarded to outstanding life science researchers. I am committed to contributing to the advancement of neuroscience technology.”

The Human Frontier Science Program (HFSP) is a prestigious international research support program in the life sciences. It is supported by Human Frontier Science Program Organization (HFSPO) which was established in 1989 through the initiative of the G7 nations and the European Union to support researchers capable of conducting creative, interdisciplinary, and collaborative international research aimed at uncovering the mechanisms of life through innovative approaches. Republic of Korea has participated as a member country since 2004.

Since the program’s initial launch, HFSP has supported over 8,500 researchers from 73 countries, including 31 Nobel laureates, earning it the reputation of the “Nobel Prize Fund” in life sciences. Including Professor Yoon, a total of 17 domestic researchers have received support from HFSP since 1990.

EE Professor Minsoo Rhu’s research team develops a simulation framework called vTrain

〈 (From left) Professor Minsoo Rhu, Ph.D. candidate Jehyeon Bang, and Dr. Yujeong 〉

Large AI models such as ChatGPT and DeepSeek are gaining attention as they’re being applied across diverse fields. These large language models (LLMs) require training on massive distributed systems composed of tens of thousands of data center GPUs. For example, the cost of training GPT-4 is estimated at approximately 140 billion won. A team of Korean researchers has developed a technology that optimizes parallelization configurations to increase GPU efficiency and significantly reduce training costs.

An EE research team led by Professor Minsoo Rhu, in collaboration with the Samsung Advanced Institute of Technology (SAIT), has developed a simulation framework called vTrain, which accurately predicts and optimizes the training time of LLMs in large-scale distributed environments.

To efficiently train LLMs, it’s crucial to identify the optimal distributed training strategy. However, the vast number of potential strategies makes real-world testing prohibitively expensive and time-consuming. As a result, companies currently rely on a limited number of empirically validated strategies, causing inefficient GPU utilization and unnecessary increases in training costs. The absence of suitable large-scale simulation technology has significantly hindered companies from effectively addressing this issue.

To overcome this limitation, Professor Rhu’s team developed vTrain, which can accurately predict training time and quickly evaluate various parallelization strategies. Through experiments conducted in multi-GPU environments, vTrain’s predictions were compared against actual measured training times, resulting in an average absolute percentage error (MAPE) of 8.37% on single-node systems and 14.73% on multi-node systems.

〈 Figure 1. Schematic diagram of the vTrain simulator architecture 〉

In collaboration with SAIT, the team has also released the vTrain framework along with over 1,500 real-world training time measurement datasets as open-source software (https://github.com/VIA-Research/vTrain) for free use by AI researchers and companies.

〈 Figure 2. Comparison of measured and predicted training times for single-node (left) and multi-node (right) systems (Figure caption as provided in the original article) 〉

Professor Rhu commented, “vTrain utilizes a profiling-based simulation approach to explore training strategies that enhance GPU utilization and reduce training costs compared to conventional empirical methods. With the open-source release, companies can now efficiently cut the costs associated with training ultra-large AI models.”

〈 Figure 3. Changes in MT-NLG training time and GPU utilization with various parallelization techniques (Figure caption as provided in the original article) 〉

This research, with Ph.D. candidate Jehyeon Bang as the first author, was presented last November at MICRO, the joint International Symposium on Microarchitecture hosted by IEEE and ACM, one of the premier conferences in computer architecture. (Paper title: “vTrain: A Simulation Framework for Evaluating Cost-Effective and Compute-Optimal Large Language Model Training”, https://doi.org/10.1109/MICRO61859.2024.00021)

This work was supported by the Ministry of Science, ICT, the National Research Foundation of Korea, the Information and Communication Technology Promotion Agency, and Samsung Electronics, as part of the SW Star Lab project for the development of core technologies in the SW computing industry.

EE Prof. Seungwon Shin’s Team Validates Cyber Risks of LLMs

〈 <(from left) Ph.D. candidate Kim Hanna, Prof. Shin Seungwon, and Ph.D. candidate Song Minkyoo 〉

Recent advancements in artificial intelligence have propelled large language models (LLMs) like ChatGPT from simple chatbots to autonomous agents. Notably, Google’s recent retraction of its previous pledge not to use AI for weapons or surveillance applications has rekindled concerns about the potential misuse of AI. In this context, the research team has demonstrated that LLM agents can be exploited for personal information collection and phishing attacks.

A joint research team, led by EE Professor Seungwon Shin and AI Professor Kimin Lee, experimentally validated the potential for LLMs to be misused in cyber attacks in real-world scenarios.

Currently, commercial LLM services—such as those offered by OpenAI and Google AI—have built-in defense mechanisms designed to prevent their use in cyber attacks. However, the research team’s experiments revealed that these defenses can be easily bypassed, enabling malicious cyber attacks.

Unlike traditional attackers who required significant time and effort to carry out such attacks, LLM agents can autonomously execute actions like personal information theft within an average of 5 to 20 seconds at a cost of only 30 to 60 won (approximately 2 to 4 cents). This efficiency has emerged as a new threat vector.

〈 Figure 1. Illustration showing the process in which an LLM agent utilizes web-based tools to generate responses according to the attacker’s (user’s) requests. 〉

According to the experimental results, the LLM agent was able to collect personal information from targeted individuals with up to 95.9% accuracy. Moreover, in an experiment where a false post was created impersonating a well-known professor, up to 93.9% of the posts were perceived as genuine.

In addition, the LLM agent was capable of generating highly sophisticated phishing emails tailored to a victim using only the victim’s email address. The experiments further revealed that the probability of participants clicking on links embedded in these phishing emails increased to 46.67%. These findings highlight the serious threat posed by AI-driven automated attacks.

Kim Hanna, the first author of the study, commented, “Our results confirm that as LLMs are endowed with more capabilities, the threat of cyber attacks increases exponentially. There is an urgent need for scalable security measures that take into account the potential of LLM agents.”

〈 Figure 2. A phishing email generated by an LLM agent (using Claude) targeted at Meta’s CEO, Mark Zuckerberg. The email was created solely based on his email address, with the LLM agent autonomously determining relevant content, sender information, and URL link text. 〉

Professor Shin stated, “We expect this research to serve as an essential foundation for improving information security and AI policy. Our team plans to collaborate with LLM service providers and research institutions to discuss robust security countermeasures.”

〈Figure 3. Experimental results showing the extent to which personal information can be collected using a Claude-based LLM agent. In this experiment, personal information of computer science professors was collected. 〉

The study, with Ph.D. candidate Kim Hanna as the first author, will be presented at the USENIX Security Symposium 2025—one of the premier international conferences in the field of computer security. (Paper title: “When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs” — DOI: 10.48550/arXiv.2410.14569)

This research was supported by the Information and Communication Technology Promotion Agency, the Ministry of Science and ICT, and the Gwangju Metropolitan City.

EE Prof. Kyung Cheol Choi Appointed as SID (Society for Information Display) Fellow

〈 Professor Kyung Cheol Choi 〉

EE Professor Kyung Cheol Choi from our department has been appointed as a Fellow of the Society for Information Display (SID). Globally, only 10 researchers have been recognized as Fellows by both the IEEE (Institute of Electrical and Electronics Engineers) and SID in the field of display technology.

SID selects only five Fellows each year, based on their industrial contributions and research achievements. Professor Kyung Cheol Choi has been appointed as a 2025 SID Fellow for his research contributions in “For pioneering development of truly wearable OLED displays using fiber and fabric substrates.”

He has previously received the Merck Award in 2018 and the UDC Innovative Research Award in 2022. In 2023, he was also recognized as an IEEE Fellow for his research achievements in flexible displays.

EE Professor Jun-Bo Yoon’s Team Achieves Human-Level Tactile Sensing with Breakthrough Pressure Sensor

〈(From left) Professor Jun-Bo Yoon, Dr. Jae-Soon Yang〉

Recent advancements in robotics have enabled machines to delicately handle fragile objects such as eggs an achievement made possible by fingertip-integrated pressure sensors that provide tactile feedback. However, even the world’s most advanced robots have struggled to accurately detect pressure in environments affected by complex external interference factors such as water, bending, or electromagnetic interference. Our research team has successfully developed a pressure sensor that operates stably without external interference even on a wet smartphone screen and achieves pressure sensing close to the level of human tactile perception.

EE Professor Jun-Bo Yoon’s research team has developed a pressure sensor capable of high-resolution pressure detection even when a smartphone screen is wet from rain or after a shower. Importantly, the sensor is immune to external interference such as “ghost touch” (erroneous touch registration) and maintains its performance under these adverse conditions.

Conventional touch systems typically employ a capacitive pressure sensor because of its simple structure and excellent durability, which makes it widely used in smartphones, wearable devices, and robotic human–machine interfaces. However, these sensors are critically vulnerable to external interference, such as water droplets, electromagnetic interference, or bending-induced deformation that can cause malfunctions.

Figure 1. (Left) Schematic illustration of a smartphone surface where water impairs proper touch registration on a rainy day. (Center) Schematic diagram showing unintended sensor malfunctions in the presence of interference. (Right) Simulation results of the electric field distribution under normal conditions and in the presence of interference; interference causes distortion of the fringe field.

To address this problem, the research team first investigated the root cause of interference in capacitive pressure sensors. They discovered that the “fringe field” generated at the sensor’s edge is extremely vulnerable to external interference.

To fundamentally resolve this issue, the team concluded that suppressing the fringe field—the source of the problem—was essential. Through theoretical analysis, they closely examined the structural variables that affect the fringe field and confirmed that narrowing the electrode gap to the order of several hundred nanometers could suppress the fringe field to below a few percent of its original level.

Figure 2. (Left) Photograph of the nanogap pressure sensor developed in this study. (Center) Schematic diagram demonstrating how the nanogap design effectively suppresses the fringe field to block external interference. (Right) Electron microscope image of the fabricated nanogap pressure sensor.

Utilizing proprietary micro/nano fabrication techniques, the research team developed a nanogap pressure sensor with an electrode gap of approximately 900 nanometers. The sensor reliably detected pressure regardless of the applied material and maintained its sensing performance even under bending or electromagnetic interference.

Moreover, by leveraging the characteristics of the developed sensor, the team implemented an artificial tactile system. Human skin employs pressure receptors known as Merkel’s discs for tactile sensing. To mimic this function, a pressure sensor technology that responds solely to pressure while remaining unresponsive to external interference was required, a condition that had proven challenging with previous technologies.

The sensor developed by Professor Yoon’s team overcomes these limitations. Its density reaches a level comparable to that of Merkel’s discs, enabling the realization of a wireless, high-precision artificial tactile system.

Figure 3. (Left) Schematic diagram comparing the human method of pressure detection with that of the interference-free, high-resolution nanogap pressure sensor designed to mimic it. (Right) Illustration of a wireless artificial tactile system utilizing the nanogap pressure sensor that can grasp objects even when water is present on the surface. The sensor remains unresponsive to water yet precisely detects pressure.

To further explore its applicability in various electronic devices, the team also developed a force touch pad system. They demonstrated that this system could obtain high-resolution measurements of pressure magnitude and distribution without interference.

Professor Yoon commented, “Our nanogap pressure sensor operates reliably without malfunctioning, even on rainy days or in sweaty conditions, unlike conventional pressure sensors. We expect this development to alleviate a common inconvenience experienced in everyday life.”

Figure 4. (Left) Schematic of the force touch pad system implemented using the nanogap pressure sensor, along with an illustration showing the sensor’s surface covered with water. (Center) Multi-touch measurement results obtained using the force touch pad system in a water-covered scenario. (Right) Three-dimensional measurement results accurately depicting pressure magnitude and distribution without interference or cross-talk from water on the sensor’s surface.

This research, led by Dr. Jae-Soon Yang, PhD Candidate Myung-Kun Chung, along with contributions from Professor Jae-Young Yoo from Sungkyunkwan University, was published in the renowned international journal Nature Communications on February 27, 2025. (Paper title: “Interference-Free Nanogap Pressure Sensor Array with High Spatial Resolution for Wireless Human-Machine Interfaces Applications”, https://doi.org/10.1038/s41467-025-57232-8)

The study was supported by the National Research Foundation of Korea’s Mid-Career Researcher Support Program and Leading Research Center Support Program.

EE Prof. Si-Hyeon Lee Appointed as Associate Editor of IEEE Transactions on Information Theory

Professor Si-Hyeon Lee

Professor Si-Hyeon Lee has been appointed as an Associate Editorof IEEE Transactions on Information Theory, the most prestigious journal in the field of information theory. Founded in 1953, IEEE Transactions on Information Theory is one of the oldest journals in the IEEE and serves as a leading platform for theoretical research on the representation, storage, transmission, processing, and learning of information. The journal particularly focuses on publishing research that explores fundamental principles and applications across various domains, including communications, compression, security, machine learning, and quantum information. As an Associate Editor, Professor Lee will play a pivotal role inmanaging the peer review process and shaping the academic direction of the journal, making significant contributions to the advancement of the field. Notably, this appointment marks only the fourth time in over 70 years since the journal’s inception that a researcher affiliated with a Korean university has been selected for this role, highlighting Professor Lee’s outstanding research achievements and international academic contributions.

Professor Lee’s primary research areas include the study of information-theoretic performance limits and the development of optimal schemes in communication, statistical inference, and machine learning, contributing to the theoretical foundations of next-generation communication and intelligent systems. Additionally, Professor Lee has served as a Technical Program Chairfor the IEEE Information Theory Workshop, a major international conference in information theory, and has been actively engaged as an IEEE Information Theory Society Distinguished Lecturer, disseminating the latest research trends to the academic community.

Dr. Taein Shin from Professor Jeongho Kim’s research lab, Selected for ‘Best Paper Award’ at DesignCon 2025 International Conference

<Dr. Taein Shin>

Dr. Taein Shin from Professor Jeongho Kim’s research lab has been selected as a recipient of the ‘Best Paper Award’ at ‘DesignCon 2025,’ a prestigious international conference in semiconductor design.

Dr. Shin had previously won the same award at ‘DesignCon 2022’ three years ago. At that time, Professor Jeongho Kim’s research lab (KAIST TERA Lab) gained significant attention from industry and academia as four of its students, including Dr. Shin, Seongguk Kim, Seonguk Choi, and Hyeyeon Kim, were honored with the Best Paper Award, which was given to only eight recipients among all submitted papers.

‘DesignCon’ is a globally recognized international conference in semiconductor and package design. Each year, researchers and engineers from leading global tech companies such as Intel, NVIDIA, Google, Micron, Rambus, Texas Instruments (TI), AMD, IBM, and ANSYS, as well as students from renowned universities worldwide, participate in this conference held in Silicon Valley, USA.

‘DesignCon’ calls for paper abstracts at the end of June each year and conducts a rigorous review process until the end of December. The submitted papers predominantly focus on practical technologies closely related to industry applications or those that can be directly implemented in products.

Among all submitted papers, only up to 20 are shortlisted as Best Paper Award nominees. The authors of these nominated papers must attend the conference in person and deliver a 45-minute oral presentation, after which a strict evaluation process determines the final eight recipients of the Best Paper Award.

Dr. Shin attended the ‘DesignCon 2025’ international conference, which was held in San Jose, Silicon Valley, from January 28 for three days. He presented his research alongside fellow KAIST TERA Lab members—Hyeyeon Kim, a Ph.D. student, and Hyunjun Ahn, an M.S. student—who were also nominated for the Best Paper Award.

A TERA Lab representative stated, “Dr. Shin’s paper was selected from over 100 papers accepted by the conference in late 2024. His contribution to technological innovation in the field was highly regarded by the judging panel.”

Dr. Shin’s paper, titled “PSIJ-Based Integrated Power Integrity Design for HBM Using Reinforcement Learning: Beyond the Target Impedance,” introduces a methodology that optimizes power integrity design for high-bandwidth memory (HBM) packages. His approach utilizes power supply noise-induced jitter (PSIJ) as a criterion, incorporating AI to optimize design parameters affecting jitter, drawing significant attention from the academic and industrial communities.

The TERA Lab representative further emphasized, “Dr. Shin’s research received high praise from the judges for overcoming the limitations of traditional impedance-based power delivery network (PDN) design by leveraging reinforcement learning and power supply noise jitter. The originality of applying AI to this field was also highly rated.”

Dr. Shin stated, “As next-generation HBM-based package systems continue to advance in speed to support large-scale AI implementations, I aim to establish a foundation for semiconductor signal and power integrity design based on the proposed methodology.”

Meanwhile, as of March 2025, Professor Jeongho Kim’s research lab comprises 27 students, including 17 master’s and 10 Ph.D. candidates. The lab is conducting research on optimizing various semiconductor package and interconnection designs in both front-end and back-end processes using AI and machine learning techniques such as reinforcement and imitation learning. Additionally, the lab is actively researching HBM-based computing architectures for large-scale AI implementations.

EE Prof. Jung-Yong Lee’s Research Team Develops Groundbreaking Avalanche Multiplication Technology in Quantum Dots

<(From left) Professor Jung-Yong Lee, Ph.D. candidate Yun Hoo Kim and Dr. Byeongsu Kim>

In a significant breakthrough for quantum technology, KAIST researchers have developed an advanced avalanche multiplication technology using colloidal quantum dots (CQDs). This innovation addresses key limitations in avalanche photodiode (APD) devices, which are crucial for applications such as quantum computing, night vision, autonomous vehicles, and space observation. *Avalanche Photodiode Devices: APD devices using crystalline semiconductors have long been employed to detect extremely faint light. However, these conventional devices suffer from high thermal noise, requiring cryogenic cooling, and lack materials with high detection efficiency in the infrared spectrum. These challenges have restricted their practicality for quantum communication and infrared sensing applications.

EE research team led by Professor Jung-Yong Lee has successfully developed an avalanche charge multiplication technology utilizing colloidal quantum dots (CQDs). This technology achieves 85-fold electron generation from a single infrared photon absorption, surpassing the limitations of conventional techniques. *Avalanche Charge Multiplication: A signal amplification method where electrons in a semiconductor, subjected to a strong electric field, gain kinetic energy and collide with adjacent atoms, generating additional electrons.

Colloidal quantum dots (CQDs), chemically synthesized semiconductor nanoparticles, are emerging as promising candidates for next-generation infrared sensors due to their solution processability and facile bandgap tunability. Unlike tranditional crystalline semiconductors, CQDs possess a unique energy structure that effectively suppresses thermal noise. However, their low charge mobility and high charge recombination rates due to surface bonds often degrade charge extraction efficiency.

The research team addressed these challenges by applying a strong electric field to accelerate electrons, gaining kinetic energy and generating additional electrons through a cascading process in neighboring quantum dots. This led to an 85-fold signal amplification under infrared irradiation at room temperature and the developed CQD-based photodetector achieved a specific detectivity of over 1.4×10¹⁴ Jones, surpassing the sensitivity of standard night vision devices by tens of thousands of times.

<Figure 1. Schematic illustration of avalanche charge amplification mechanisms

in quantum dot devices and the specific detectivity performance of the quantum dot-based avalanche photodiode.>

Infrared photodetectors are essential for a wide range of applications, including autonomous vehicles, quantum computing, and advanced medical imaging. However, traditional quantum dot-based technologies have long been limited by low sensitivity and high noise.

This research represents a paradigm-shifting technological advance, establishing a strong foundation for South Korea to lead the global quantum technology market by securing core innovations in quantum sensing and infrared detection.

Dr. Byeongsu Kim, the first author of the study, emphasized the groundbreaking nature of this work: “The quantum dot avalanche device is an entirely novel research area, never previously reported. This foundational technology has the potential to drive ventures that will lead the global markets in autonomous vehicles, quantum computing, and medical imaging.”

Dr. Byeongsu Kim from KAIST’s Information and Electronics Research Institute, Dr. Sang Yeon Lee from IMEC, and Dr. Hyunseok Ko from the Korea Institute of Ceramic Engineering and Technology contributed as co-first authors of the study. Their findings was published in Nature Nanotechnology on December 18, under the title: “Ultrahigh-gain colloidal quantum dot infrared avalanche photodetectors” (DOI: 10.1038/s41565-024-01831-x).

This research was supported by the National Research Foundation of Korea, including Nano-Material Technology Development Program, Strategic Research Laboratory Program for Future Displays, and Individual Basic Research Program.

EE Prof. Seunghyup Yoo’s research team Develops Wearable Carbon Dioxide Sensor to Enable Real-time Apnea Diagnosis

<(From the left) The School of Electrical Engineering, Ph.D. candidate DongHo Choi, Professor Seunghyup Yoo, and Department of Materials Science and Engineering, Bachelor’s candidate MinJae Kim >

Carbon dioxide (CO2) is a major respiratory metabolite, and continuous monitoring of CO2 concentration in exhaled breath is not only an important indicator for early detection and diagnosis of respiratory and circulatory system diseases, but can also be widely used for monitoring personal exercise status. KAIST researchers succeeded in accurately measuring CO2 concentration by attaching it to the inside of a mask.

EE Professor Seunghyup Yoo’s research team in the School of Electrical Engineering have created a breakthrough wearable CO₂ sensor. This new device enables real-time breath monitoring while maintaining low power consumption and high-speed performance.

Traditional non-invasive CO₂ sensors have been hampered by their bulky size and high power requirements. While optochemical sensors that use fluorescent molecules offer promising advantages in terms of size and weight, they also face a significant challenge: the fluorescent dyes tend to degrade over time when exposed to light. This instability has limited their practical use in wearable healthcare devices. As these optochemical sensors work by measuring changes in fluorescence intensity, which decreases with CO₂ concentration, the key to their effectiveness lies in accurately detecting these fluorescence variations over a sufficiently long period of time.

To address these challenges, the research team engineered a low-power CO₂ sensor that incorporates an organic photodiode that surrounds an LED. This design greatly enhances light collection efficiency and minimizes the exposure of fluorescent molecules to excitation light for a given level of signal. As a result, the device achieves power consumption of just 171 μW, which is substantially lower than the several milliwatts consumed by existing optochemical CO₂ sensors.

< Figure 1. Structure and operating principle of the developed optochemical carbon dioxide (CO2) sensor. Light emitted from the LED is converted into fluorescence through the fluorescent film, reflected from the light scattering layer, and incident on the organic photodiode. CO2 reacts with a small amount of water inside the fluorescent film to form carbonic acid (H2CO3), which increases the concentration of hydrogen ions (H+), and the fluorescence intensity due to 470 nm excitation light decreases. The circular organic photodiode with high light collection efficiency effectively detects changes in fluorescence intensity, lowers the power required light up the LED, and reduces light-induced deterioration. >

The research team further elucidated the photodegradation path of fluorescent molecules used in CO₂ sensors. They uncovered the cause of the increase in error rates over time in photochemical sensors, and proposed an optical design method to mitigate these errors.

Building on these insights, the research team developed a sensor that significantly reduces errors caused by photodegradation, a persistent issue with previous photochemical sensors. Impressively, the new sensor maintains continuous functionality for up to nine hours—far surpassing the 20-minute lifespan of existing technologies—and can be reused multiple times simply by replacing the CO₂-detecting fluorescent film.

< Figure 2. Wearable smart mask and real-time breathing monitoring. The fabricated sensor module consists of four elements (①: gas-permeable light-scattering layer, ②: color filter and organic photodiode, ③: light-emitting diode, ④: CO2-detecting fluorescent film). The thin and light sensor (D1: 400 nm, D2: 470 nm) is attached to the inside of the mask to monitor the wearer’s breathing in real time. >

The newly developed sensor, which is lightweight (0.12 g), thin (0.7 mm), and flexible, was effectively integrated inside a face mask. It boasts fast response times and high resolution, enabling it to monitor respiratory rates by distinguishing between inhalation and exhalation in real-time.

< The developed sensor attached to the inside of the mask >

Professor Seunghyup Yoo commented, “The low power consumption, high stability, and flexibility of the developed sensor make it highly suitable for wearable devices. It holds great potential for the early diagnosis of various conditions, including hypercapnia, chronic obstructive pulmonary disease, and sleep apnea.” He also highlighted its utility in environments with high dust levels or where masks are worn for extended periods, such as during seasonal changes, noting its potential to alleviate the side effects caused by rebreathing.

This groundbreaking research was conducted with the involvement of Minjae Kim, an undergraduate student from Department of Materials Science and Engineering, and Dongho Choi, a doctoral student from the School of Electrical Engineering, as joint first authors, and published in the online version of Cell’s sister journal, Device, on the 22nd of last month. (Paper title: Ultralow-power carbon dioxide sensor for real-time breath monitoring) DOI: https://doi.org/10.1016/j.device.2024.100681

This study was supported by the Ministry of Trade, Industry and Energy’s Materials and Components Technology Development Project, the National Research Foundation of Korea’s Original Technology Development Project, and the KAIST Undergraduate Research Participation (URP) Project.

EE Prof. Joungho Kim Selected as the Recipient of the 2025 Kang Dae Won Award in the Circuits and Systems Category

EE Professor Joungho Kim has been selected as the recipient of the 2025 Kang Dae Won Award in the Circuits and Systems category, in recognition of his contributions to the development of High Bandwidth Memory (HBM).

Established to honor the late Dr. Kang Dae Won, the pioneer behind the world’s first MOSFET and floating gate, the Kang Dae Won Award has been recognizing outstanding researchers who continue his legacy since the 24th KCS in 2017.

Professor Joungho Kim, often referred to as the “Father of HBM,” is a world-renowned authority in AI semiconductor technology. For over two decades, he has played a pivotal role in leading HBM-related design technologies worldwide. His research in Through-Silicon Via (TSV), interposers, signal integrity (SI), and power integrity (PI) design has been recognized globally for its originality and impact. Notably, since 2010, he has been directly involved in the design and commercialization of HBM, which is credited with enabling the modern AI era.

Recently, he has been at the forefront of research on next-generation HBM architectures, including HBM4, HBM5, and HBM6. Furthermore, he is actively working on automating HBM design using artificial intelligence. In particular, he is pioneering research at a global level by integrating reinforcement learning and generative AI to optimize HBM’s electrical and thermal performance, leading advancements in this field.

An IEEE Fellow, Professor Kim has been recognized for his significant contributions to the semiconductor industry through research and education. In recognition of his achievements, he has received prestigious awards, including the KAIST Academic Award, KAIST Research Award, KAIST International Collaboration Award, and the IEEE Technical Achievement Award. He has also won over 20 Best Paper Awards at major international conferences, further demonstrating his academic excellence.

This award is presented by the Permanent Steering Committee of the Korean Conference on Semiconductors (KCS). The award ceremony will take place in the afternoon of February 13 during the opening session of the 32nd Korean Conference on Semiconductors (KCS 2025) at High1 Grand Hotel in Gangwon-do. The event is co-hosted by the Korea Semiconductor Industry Association (KSIA), the Korea Semiconductor Research Association (KSRA), and DB HiTek.

Archives: 연구 > 연구성과

EE Professor Young-Gyu Yoon Selected as a Recipient of the Human Frontier Science Program (HFSP) Award

EE Professor Minsoo Rhu’s research team develops a simulation framework called vTrain

EE Prof. Seungwon Shin’s Team Validates Cyber Risks of LLMs

EE Prof. Kyung Cheol Choi Appointed as SID (Society for Information Display) Fellow

EE Professor Jun-Bo Yoon’s Team Achieves Human-Level Tactile Sensing with Breakthrough Pressure Sensor

EE Prof. Si-Hyeon Lee Appointed as Associate Editor of IEEE Transactions on Information Theory

Dr. Taein Shin from Professor Jeongho Kim’s research lab, Selected for ‘Best Paper Award’ at DesignCon 2025 International Conference

EE Prof. Jung-Yong Lee’s Research Team Develops Groundbreaking Avalanche Multiplication Technology in Quantum Dots

EE Prof. Seunghyup Yoo’s research team Develops Wearable Carbon Dioxide Sensor to Enable Real-time Apnea Diagnosis

EE Prof. Joungho Kim Selected as the Recipient of the 2025 Kang Dae Won Award in the Circuits and Systems Category

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