< Professor Sanghun Jeon, Winner of the 2024 Haedong Outstanding Paper Award >
Professor Sanghun Jeon from the Department of Electrical Engineering has been selected as the top recipient in the academic category of the 2024 Haedong Outstanding Paper Award, hosted by The Institute of Electronics and Information Engineers (IEIE) (President Chungyong Lee, Yonsei University) and sponsored by the Haedong Science Foundation.
The award ceremony took place on November 25 at 6 PM in the Grand Ballroom of the Convention Tower at the High1 Resort. The Haedong Awards by The Institute of Electronics and Information Engineers consist of three categories: Academic Award, Technology Award, and Young Engineer Award. The JSTS Academic Award specifically recognizes the best paper published in the JSTS journal over the past three years.
Professor Sanghun Jeon was recognized for his paper, “Ferroelectricity in Al2O3/Hf0.5Zr0.5O2 Bilayer Stack: Role of Dielectric Layer Thickness and Annealing Temperature,” which proposes dielectric stacking structures and design guidelines to enhance the thermal stability, performance, and reliability of hafnia ferroelectrics – a material gaining attention as a next-generation memory and storage device.
Once again, congratulations to Professor Sanghun Jeon for elevating the reputation of the Department of Electrical Engineering
< From left to right: Master’s candidate Woo-Hyun Hwang and undergraduate Jae-Jun Lee >
Master’s candidate Woo-Hyun Hwang and undergraduate student Jae-Jun Lee from Professor Seonghwan Cho’s lab achieved remarkable success by winning the Grand Prize at the ‘2024 Korean University Semiconductor Circuit Design Competition Award Ceremony’, held on December 11 at the Grand InterContinental Seoul Parnas Hotel.
The ‘Korean University Semiconductor Circuit Design Competition’, organized by the Institute of Semiconductor Engineers, aims to nurture IC circuit design skills among university students nationwide, discover creative ideas, and is supported by numerous semiconductor-related companies.
< From left to right: Jae-Jun Lee and Woo-Hyun Hwang at the ceremony>
At the awards ceremony on December 11, the Grand Prize-winning project titled ‘A Chip-Scale Operable Magnetic Resonance-Based Two-Coil Wireless Communication System’ was developed by Woo-Hyun Hwang and Jae-Jun Lee.
The project received high praise for its creativity, complexity, and level of completion. In particular, the innovative concept of implementing a “magnetic resonance-based two-coil system” at a chip scale, and the ability to achieve effective design with high completion in challenging wireless communication environments, were recognized and led to the Grand Prize award.
<(From left) Ph.D. Candidate Mintaek Oh, Professor Jinseok Choi>
Mintaek Oh, a Ph.D. Candidate in Professor Jinseok Choi’s lab at our department, achieved a Silver Award at the ‘2024 IEEE Student Paper Contest’ held by IEEE Seoul Section on December 7th.
The ‘IEEE Student Paper Contest’ is a prestigious competition organized by the IEEE Seoul Section that identifies outstanding research papers across all fields of electrical and electronic engineering and recognizes students’ research capabilities. Students who present outstanding papers at this year’s contest will be granted a privilege to participate in 2025 IEEE Region 10 Student Paper Contest.
<Mintaek Oh, recipient of the Silver Award at the ‘2024 IEEE Student Paper Contest’>
The award-winning paper titled “Multi-RIS-Aided Beamforming Design for MU-MIMO Systems with Imperfect CSIT” presents research on beamforming design considering practical constraints in wireless communication systems utilizing the RIS, which is emerging as a key technology for next-generation wireless communication systems. This research proposes an optimization approach for MU-MIMO system performance using multiple RISs under imperfect channel state information.
In this competition, amid competition spanning all fields of electrical engineering in Korea, the research excellence in wireless communications was recognized with the Silver Award.
A research team led by Professor Junmo Kim from the Department of Electrical Engineering at KAIST has developed an innovative AI technology that can envision and understand how images change, similar to how humans imagine transformations like rotation or recoloring. This breakthrough goes beyond simply analyzing images, enabling the AI to comprehend and express the processes involved in transforming visual data. The technology holds promise for diverse applications, including medical imaging, autonomous driving, and robotics, where precision and adaptability are essential.
AI That Imagine Changes Like Humans (Understands How Images Change, Like Humans)
The newly developed technology, Self-supervised Transformation Learning (STL), focuses on enabling AI to learn how images transform. STL operates without relying on human-provided labels; instead, it learns transformations by comparing original images with their transformed versions. It independently recognizes changes such as, “This has been rotated,” or, “The color has changed.” This process parallels the way humans observe, imagine, and interpret variations in visual data.
Illustration of the roles of the three representation learning approaches that constitute STL: (a) distinguishing images regardless of transformations, (b) aligning transformation representations for the same transformation applied to different images, and (c) ensuring that relationships between representations of transformed variants of the same image reflect the actual transformation. STL integrates all these roles for comprehensive learning
Overcoming the Limitations of Conventional Methods
Traditional AI systems often struggle with subtle transformations, focusing primarily on capturing large, overarching features while ignoring finer details. This limitation becomes a significant challenge in scenarios where precise understanding of intricate changes is crucial.
STL addresses this gap by learning to encode even the smallest transformations in an image into its feature space—a conceptual map representing the relationships between different data points. Rather than ignoring these changes, STL incorporates them into its feature representations, enabling more accurate and nuanced outcomes.
For example, STL excels at recognizing specific alterations, such as random cropping, brightness adjustments, and color modifications, achieving performance improvements of up to 42% over conventional methods. It is particularly adept at handling complex transformations that were previously difficult for AI to manage.
Examples of transformations often ignored by existing methods. These transformations, though subtle, may carry important differences.
Smarter AI for Broader Applications
What sets STL apart is its ability to not only understand visual content but also learn and represent transformations themselves. This capability allows STL to detect subtle changes in medical images, such as CT scans, and better interpret diverse conditions in autonomous driving. By incorporating transformations into its understanding, STL can deliver safer and more precise results across various applications.
Toward Human-Like Understanding
“STL represents a significant leap forward in AI technology, closely mirroring the way humans perceive and interpret changes in images,” said Professor Junmo Kim. “This approach has the potential to drive innovations in fields such as healthcare, robotics, and self-driving cars, where understanding transformations is critical.”
The research, conducted by Jaemyung Yu, a PhD candidate at KAIST as the first author, was presented at NeurIPS 2024, one of the world’s leading AI conferences, under the title Self-supervised Transformation Learning for Equivariant Representations. It was supported by the Ministry of Science and ICT through the Institute of Information and Communications Technology Planning and Evaluation (IITP) as part of the SW StarLab program (No. RS-2024-00439020, Development of Sustainable Real-time Multimodal Interactive Generative AI).
<(from left) Professor Chan-Hyun Youn, Ph.D. candidate Jinhyuk Jang>
Professor Chan-Hyun Youn’s research team has developed a novel approach to Machine Unlearning, a critical aspect of AI safety, by introducing the past weight prediction model InvWNN. This model aims to selectively remove the influence of problematic data from AI models trained on such data. Existing methods often require access to the entire training dataset or face challenges with performance degradation. To address these issues, the team proposed a new method that leverages weight history to predict past weights and iteratively applies this process to progressively eliminate the influence of the data.
<Figure 1: Machine Unlearning Process of the Proposed InvWNN and Unlearning Trajectory>
This method gradually removes the problematic data’s influence through iterative fine-tuning on the problematic data and the past weight prediction. Notably, this approach operates effectively without access to the remaining data and can be applied to various datasets and architectures. Compared to existing methods, the proposed technique excels at accurately removing unnecessary knowledge from training data while minimizing side effects. Furthermore, it has been validated that this method can be directly applied to a variety of tasks without requiring additional procedures.
The research team demonstrated the high performance of their method across various benchmarks, showcasing its potential to significantly expand the practical applicability of machine unlearning technology. These findings will be presented at the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025), one of the premier international conferences in the field of artificial intelligence, to be held in the United States in February next year, with the title “Learning to Rewind via Iterative Prediction of Past Weights for Unlearning.”
Professor Junil Choi of our School of Electrical Engineering has been awarded the 6th Next-Generation Scientist Award (IT field), co-hosted by the Korean Academy of Science and Technology (KAST) and the Korean Association of University Presidents, and sponsored by the S-OIL Science and Culture Foundation.
The Korean Academy of Science and Technology (https://kast.or.kr) has been running the S-OIL Awards program since 2011 with support from the S-OIL Science and Culture Foundation. This initiative identifies and recognizes young scientists in the fields of basic science and engineering to foster them as key contributors to national science and technology in the 21st century.
The S-OIL Next-Generation Scientist Award is presented to researchers aged 45 or younger. Candidates are evaluated based on their top 10 representative research papers published over the last 10 years, excluding those from their doctoral studies or postdoctoral periods.
Professor Junil Choi received the award on December 5, 2024, in recognition of his outstanding research in communication systems leveraging machine learning and his contributions to 6G wireless communication systems.
The School of Electrical Engineering at KAIST has demonstrated remarkable consistency, with Professors Hyun-Joo Lee (2022), Jae-Woong Jeong (2023), and now Junil Choi (2024) receiving the Next-Generation Scientist Award in the IT field for three consecutive years.
<(from left) Ph.D. candidates Eunyoung Jang and Kiup Kim> Ph.D. candidates Eunyoung Jang and Kiup Kim from Professor Hyunjoo Jenny Lee’s laboratory in our department were awarded the Best Paper Award in graduate students’ poster presentations at the ‘2024 Micro and Nano Systems Fall Conference’. This event was held on November 21 at the Asti Hotel in Busan.
The ‘Micro and Nano Systems Fall Conference’ has been an annual event since 2020, aimed at fostering academic and personal exchanges.
The title of the winning paper is ‘3D Organoid Multi-functional Monitoring Platforms for Real-time and Non-invasive Analysis’. The paper, contributed to by both Eunyoung Jang and Kiup Kim, was recognized for its excellence within the Medical MEMS category, one of the four academic divisions at the conference.
Professor of Electrical Engineering Hyunjoo Lee has been honored with the Young Researcher Award at the 2024 Korean Society for Therapeutic Ultrasound (KSTU) conference.
This prestigious award recognizes outstanding research achievements in the field of therapeutic ultrasound and is granted to one researcher under the age of 43. The Korean Society for Therapeutic Ultrasound is an academic organization dedicated to the advancement of therapeutic ultrasound technology through academic research and innovation. Its initiatives include promoting research and clinical applications of diagnostic and therapeutic ultrasound technologies while fostering interdisciplinary collaboration and exchange among related fields.
Therapeutic ultrasound, in particular, is a groundbreaking technology that uses ultrasound energy to stimulate tissues or treat pathological conditions. It has drawn significant attention for its innovative applications in various medical domains, including cancer treatment, neural stimulation, and thrombolysis. By strengthening the academic foundation of therapeutic ultrasound, the society aims to facilitate collaboration between researchers and clinicians, contributing to advancements in medical technology and improving patient quality of life.
Professor Lee has been conducting research on neural interfaces and brain-computer interfaces, focusing on developing non-invasive brain stimulation technologies using ultrasound for bidirectional neural interfaces and the treatment of neurological disorders. This award acknowledges Professor Lee’s contributions to the field of therapeutic ultrasound and highlights her potential as a next-generation leader in the domain.
<Photo. (From left) Professor SangHyeon Kim, Professor Dae-Myung Geum of Inha University (formerly a KAIST postdoctoral researcher) and Dr. Jinha Lim (currently a postdoctoral researcher at Yale University)
A joint research team from Korea and the United States has developed next-generation, high-resolution image sensor technology with higher power efficiency and a smaller size compared to existing sensors. Notably, they have secured foundational technology for ultra-high-resolution shortwave infrared (SWIR) image sensors, an area currently dominated by Sony, paving the way for future market entry.
Research team led by Professor SangHyeon Kim from the School of Electrical Engineering, in collaboration with Inha University and Yale University in the U.S., has developed an ultra-thin broadband photodiode (PD), marking a significant breakthrough in high-performance image sensor technology.
This research drastically improves the trade-off between the absorption layer thickness and quantum efficiency found in conventional photodiode technology. Specifically, it achieved high quantum efficiency of over 70% even in an absorption layer thinner than one micrometer (μm), reducing the thickness of the absorption layer by approximately 70% compared to existing technologies.
A thinner absorption layer simplifies pixel processing, allowing for higher resolution and smoother carrier diffusion, which is advantageous for light carrier acquisition while also reducing the cost. However, a fundamental issue with thinner absorption layers is the reduced absorption of long-wavelength light.
< Figure 1. Schematic diagram of the InGaAs photodiode image sensor integrated on the Guided-Mode Resonance (GMR) structure proposed in this study (left), a photograph of the fabricated wafer, and a scanning electron microscope (SEM) image of the periodic patterns (right) >
The research team introduced a guided-mode resonance (GMR) structure* that enables high-efficiency light absorption across a wide spectral range from 400 nanometers (nm) to 1,700 nanometers (nm). This wavelength range includes not only visible light but also light the SWIR region, making it valuable for various industrial applications. *Guided-Mode Resonance (GMR) Structure: A concept used in electromagnetics, a phenomenon in which a specific (light) wave resonates (forming a strong electric/magnetic field) at a specific wavelength. Since energy is maximized under these conditions, it has been used to increase antenna or radar efficiency.
The improved performance in the SWIR region is expected to play a significant role in developing next-generation image sensors with increasingly high resolutions. The GMR structure, in particular, holds potential for further enhancing resolution and other performance metrics through hybrid integration and monolithic 3D integration with complementary metal-oxide-semiconductor (CMOS)-based readout integrated circuits (ROIC).
< Figure 2. Benchmark for state-of-the-art InGaAs-based SWIR pixels with simulated EQE lines as a function of TAL variation. Performance is maintained while reducing the absorption layer thickness from 2.1 micrometers or more to 1 micrometer or less while reducing it by 50% to 70% >
The research team has significantly enhanced international competitiveness in low-power devices and ultra-high-resolution imaging technology, opening up possibilities for applications in digital cameras, security systems, medical and industrial image sensors, as well as future ultra-high-resolution sensors for autonomous driving, aerospace, and satellite observation.
Professor Sang Hyun Kim, the lead researcher, commented, “This research demonstrates that significantly higher performance than existing technologies can be achieved even with ultra-thin absorption layers.”
< Figure 3. Top optical microscope image and cross-sectional scanning electron microscope image of the InGaAs photodiode image sensor fabricated on the GMR structure (left). Improved quantum efficiency performance of the ultra-thin image sensor (red) fabricated with the technology proposed in this study (right) >
The results of this research were published on 15th of November, in the prestigious international journal Light: Science & Applications (JCR 2.9%, IF=20.6), with Professor Dae-Myung Geum of Inha University (formerly a KAIST postdoctoral researcher) and Dr. Jinha Lim (currently a postdoctoral researcher at Yale University) as co-first authors. (Paper title: “Highly-efficient (>70%) and Wide-spectral (400 nm -1700 nm) sub-micron-thick InGaAs photodiodes for future high-resolution image sensors”)
This study was supported by the National Research Foundation of Korea.
PhD candidate Jiwan Kim, and undergraduate Hoheon Jung from Professor Ian Oakley’s lab in the Department of Electrical Engineering, won the Best People’s Choice Award at the Student Innovation Contest held as part of the ‘ACM UIST (ACM Symposium on User Interface Software and Technology)’ in Pittsburgh, USA, from October 13 to 16. The Best People’s Choice Award is given to the project that garners the most enthusiasm and support from attendees during the conference.
<Awarded certificate and trophy>
The ‘ACM UIST’ is a leading international conference in the field of human-computer interaction. Each year, the Student Innovation Contest invites teams to present innovative ideas using cutting-edge hardware just before its release. This year’s theme involved creating and demonstrating interactive devices for the future using the Gen-M Kit from Seeed Studio. After a competitive preliminary round, eight teams from prestigious institutions, including Carnegie Mellon University, the University of Toronto, and the University of Hong Kong, reached the finals alongside our university’s team.
Jiwan Kim and Hoheon Jung cited the famous quote by novelist Arthur C. Clarke, known for works like 2001: A Space Odyssey: “Any sufficiently advanced technology is indistinguishable from magic.” Inspired by this idea, they developed a wearable device that provides an experience similar to superpowers.
The glove they developed uses surface acoustic waves, radar, and ultrasound to create features such as eavesdropping to hear sounds through walls, enhanced senses to detect nearby movements with closed eyes, and telekinesis to levitate small objects in the air.
Jiwan Kim remarked, “Some might view this as simply implementing technology for amusement, but I believe that fun is also an essential direction for scientific and technological advancement. We focused on interpreting various sensing technologies in the most entertaining way possible and demonstrating them accordingly.”
