GE
-
“Cross-Generation Collaborative Labs” for Semiconductor, Chemistry, and Computer Science Opened
< Photo of Professor Hoi-Jun Yoo (center) of the School of Electrical Engineering at the signboard unveiling ceremony >
KAIST held a ceremony to mark the opening of three additional ‘Cross-Generation Collaborative Labs’ on the morning of January 7th, 2025.
The “Next-Generation AI Semiconductor System Lab” by Professor Hoi-Jun Yoo of the School of Electrical Engineering, the “Molecular Spectroscopy and Chemical Dynamics Lab” by Professor Sang Kyu Kim of the Department of Chemistry, and the “Advanced Data Computing Lab” by Professor Sue Bok Moon of the School of Computer Science are the three new labs given the honored titled of the “Cross-Generation Collaborative Lab”.
The Cross-Generation Collaborative Lab is KAIST’s unique system that was set up to facilitate the collaboration between retiring professors and junior professors to continue the achievements and know-how the elders have accumulated over their academic career. Since its introduction in 2018, nine labs have been named to be the Cross-Generation Labs, and this year’s new addition brings the total up to twelve.
The ‘Next-Generation AI Semiconductor System Lab’ led by Professor Hoi-Jun Yoo will be operated by Professor Joo-Young Kim of the same school.
Professor Hoi-Jun Yoo is a world-renowned scholar with outstanding research achievements in the field of on-device AI semiconductor design. Professor Joo-Young Kim is an up-and-coming researcher studying large language models and design of AI semiconductors for server computers, and is currently researching technologies to design PIM (Processing-in-Memory), a core technology in the field of AI semiconductors.
Their research goal is to systematically collaborate and transfer next-generation AI semiconductor design technology, including brain-mimicking AI algorithms such as deep neural networks and generative AI, to integrate core technologies, and to maximize the usability of R&D outputs, thereby further solidifying the position of Korean AI semiconductor companies in the global market.
Professor Hoi-Jun Yoo said, “I believe that, we will be able to present a development direction of for the next-generation AI semiconductors industries at home and abroad through collaborative research and play a key role in transferring and expanding global leadership.”
< Professor Sang Kyu Kim of the Department of Chemistry (middle), at the signboard unveiling ceremony for his laboratory >
The “Molecular Spectroscopy and Chemical Dynamics Laboratory”, where Professor Sang Kyu Kim of the Department of Chemistry is in charge, will be operated by Professor Tae Kyu Kim of the same department, and another professor in the field of spectroscopy and dynamics will join in the future.
Professor Sang Kyu Kim has secured technologies for developing unique experimental equipment based on ultrashort lasers and supersonic molecular beams, and is a world leader who has been creatively pioneering new fields of experimental physical chemistry.
The research goal is to describe chemical reactions and verify from a quantum mechanical perspective and introduce new theories and technologies to pursue a complete understanding of the principles of chemical reactions. In addition, the accompanying basic scientific knowledge will be applied to the design of new materials.
Professor Sang Kyu Kim said, “I am very happy to be able to pass on the research infrastructure to the next generation through this system, and I will continue to nurture it to grow into a world-class research lab through trans-generational collaborative research.”
< Photo of Professor Sue Bok Moon (center) at the signboard unveiling ceremony by the School of Computing >
Lastly, the “Advanced Data Computing Lab” led by Professor Sue Bok Moon is joined by Professor Mee Young Cha of the same school and Professor Wonjae Lee of the Graduate School of Culture Technology.
Professor Sue Bok Moon showed the infinite possibilities of large-scale data-based social network research through Cyworld, YouTube, and Twitter, and had a great influence on related fields beyond the field of computer science.
Professor Mee Young Cha is a data scientist who analyzes difficult social issues such as misinformation, poverty, and disaster detection using big data-based AI. She is the first Korean to be recognized for her achievements as the director of the Max Planck Institute in Germany, a world-class basic science research institute. Therefore, there is high expectation for synergy effects from overseas collaborative research and technology transfer and sharing among the participating professors of the collaborative research lab. Professor Wonjae Lee is researching dynamic interaction analysis between science and technology using structural topic models.
They plan to conduct research aimed at improving the analysis and understanding of negative influences occurring online, and in particular, developing a hateful precursor detection model using emotions and morality to preemptively block hateful expressions.
Professor Sue Bok Moon said, “Through this collaborative research lab, we will play a key role in conducting in-depth collaborative research on unexpected negative influences in the AI era so that we can have a high level of competitiveness worldwide.”
The ceremonies for the unveiling of the new Cross-Generation Collaborative Lab signboard were held in front of each lab from 10:00 AM on the 7th, in the attendance of President Kwang Hyung Lee, Senior Vice President for Research Sang Yup Lee, and other key officials of KAIST and the new staff members to join the laboratories.
2025.01.07 View 2224 -
KAIST develops ‘Hoverbike’ to roam the future skies
< Photo 1. A group photo of the research team >
Hoverbike is a kind of next-generation mobility that can complement the existing transportation system and can be used as an air transportation means without traffic congestion through high-weight payloads and long-distance flights. It is expected that domestic researchers will contribute to the development of the domestic PAV* and UAM markets by developing a domestically developed manned/unmanned hybrid aircraft that escapes dependence on foreign technology through the development of a high-performance hoverbike.
*PAV: Personal Aerial Vehicle. It is a key element of future urban air mobility (UAM, Urban Air Mobility) and constitutes an important part of the next-generation transportation system.
KAIST (President Kwang-Hyung Lee) announced on the 27th of December that the research team of Professor Hyochoong Bang of the Department of Aerospace Engineering successfully developed the core technology of a highly reliable multipurpose vertical takeoff and landing hoverbike that can be operated by both manned and unmanned vehicles.
This research was participated by the research teams of Professor Jae-Hung Han, Professor Ji-yun Lee, Professor Jae-myung Ahn, Professor Han-Lim Choi, and Professor Chang-Hun Lee of the Department of Aerospace Engineering at KAIST, Professor Dongjin Lee of the Department of Unmanned Aerial Vehicles at Hanseo University, and Professor Jong-Oh Park of the Department of Electronics Engineering at Dong-A University.
The research team secured key technologies related to the optimal design of a multipurpose aircraft, hybrid propulsion system, highly reliable precision navigation and flight control system, autonomous flight, and fault detection for the development of a high-performance hoverbike.
< Figure 1. Key features of high-reliability multi-purpose hoverbike >
The hoverbike platform introduced a gasoline engine-based hybrid system to overcome the shortcomings of battery-based drones, achieving approximately 60% better performance and maximum payload weight compared to overseas technology levels. Through this, it is expected to be utilized in various fields such as emergency supply delivery, logistics, and rescue activities for civilian use, and military transport and mission support for military use.
The navigation system was applied by implementing multi-sensor fusion technology based on DGPS/INS* to enable stable flight even in environments without GPS or with weak signals using high-reliability precision navigation technology.
*DGPS/INS: Navigation solution combining high accuracy of Differential GPS (DGPS) and Inertial Navigation System (INS)
In addition, high-reliability flight control technology was developed to enable reliable maneuvering even under external factors such as payload and wind, and model uncertainty, and fault detection technology was also developed.
A guidance technique to automatically land on a helipad after selecting a safe automatic landing area by configuring a high-reliability autonomous flight system was implemented with high accuracy. Stable operation is possible even in complex environments through obstacle avoidance and automatic landing autonomous flight technology.
< Figure 2. Hoverbike prototype model >
Professor Hyochoong Bang, the research director, emphasized, “We have proven the high practicality of the hoverbike in various environments through high-reliability flight control and precision navigation technology.” He added, “The hoverbike is a promising research result that can not only provide a major path leading to PAVs and future aircraft, but also surpass existing drone technology by several levels. This achievement is even more meaningful because it is the result of five years of effort by eight joint research teams, including the project’s practitioners, PhD students Kwangwoo Jang and Hyungjoo Ahn.”
This study aims to secure core technologies for manned/unmanned multipurpose hoverbikes that can be utilized as new concept aircraft in the defense and civilian sectors. It started as the Defense Acquisition Program Administration’s Defense Technology for Future Challenge Research and Development Project in 2019 and was completed in 2024 under the management of the Agency for Defense Development. It is scheduled to be exhibited for the first time at the 2025 Drone Show Korea (DSK2025), which will be held at BEXCO in Busan from February 26 to 28, 2025.
2024.12.27 View 2558 -
KAIST Develops Foundational Technology to Revert Cancer Cells to Normal Cells
Despite the development of numerous cancer treatment technologies, the common goal of current cancer therapies is to eliminate cancer cells. This approach, however, faces fundamental limitations, including cancer cells developing resistance and returning, as well as severe side effects from the destruction of healthy cells.
< (From top left) Bio and Brain Engineering PhD candidates Juhee Kim, Jeong-Ryeol Gong, Chun-Kyung Lee, and Hoon-Min Kim posed for a group photo with Professor Kwang-Hyun Cho >
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of December that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering has developed a groundbreaking technology that can treat colon cancer by converting cancer cells into a state resembling normal colon cells without killing them, thus avoiding side effects.
The research team focused on the observation that during the oncogenesis process, normal cells regress along their differentiation trajectory. Building on this insight, they developed a technology to create a digital twin of the gene network associated with the differentiation trajectory of normal cells.
< Figure 1. Technology for creating a digital twin of a gene network from single-cell transcriptome data of a normal cell differentiation trajectory. Professor Kwang-Hyun Cho's research team developed a digital twin creation technology that precisely observes the dynamics of gene regulatory relationships during the process of normal cells differentiating along a differentiation trajectory and analyzes the relationships among key genes to build a mathematical model that can be simulated (A-F). In addition, they developed a technology to discover key regulatory factors that control the differentiation trajectory of normal cells by simulating and analyzing this digital twin. >
< Figure 2. Digital twin simulation simulating the differentiation trajectory of normal colon cells. The dynamics of single-cell transcriptome data for the differentiation trajectory of normal colon cells were analyzed (A) and a digital twin of the gene network was developed representing the regulatory relationships of key genes in this differentiation trajectory (B). The simulation results of the digital twin confirm that it readily reproduces the dynamics of single-cell transcriptome data (C, D). >
Through simulation analysis, the team systematically identified master molecular switches that induce normal cell differentiation. When these switches were applied to colon cancer cells, the cancer cells reverted to a normal-like state, a result confirmed through molecular and cellular experiments as well as animal studies.
< Figure 3. Discovery of top-level key control factors that induce differentiation of normal colon cells. By applying control factor discovery technology to the digital twin model, three genes, HDAC2, FOXA2, and MYB, were discovered as key control factors that induce differentiation of normal colon cells (A, B). The results of simulation analysis of the regulatory effects of the discovered control factors through the digital twin confirmed that they could induce complete differentiation of colon cells (C). >
< Figure 4. Verification of the effect of the key control factors discovered using colon cancer cells and animal experiments on the reversibility of colon cancer. The key control factors of the normal colon cell differentiation trajectory discovered through digital twin simulation analysis were applied to actual colon cancer cells and colon cancer mouse animal models to experimentally verify the effect of cancer reversibility. The key control factors significantly reduced the proliferation of three colon cancer cell lines (A), and this was confirmed in the same way in animal models (B-D). >
This research demonstrates that cancer cell reversion can be systematically achieved by analyzing and utilizing the digital twin of the cancer cell gene network, rather than relying on serendipitous discoveries. The findings hold significant promise for developing reversible cancer therapies that can be applied to various types of cancer.
< Figure 5. The change in overall gene expression was confirmed through the regulation of the identified key regulatory factors, which converted the state of colon cancer cells to that of normal colon cells. The transcriptomes of colon cancer tissues and normal colon tissues from more than 400 colon cancer patients were compared with the transcriptomes of colon cancer cell lines and reversible colon cancer cell lines, respectively. The comparison results confirmed that the regulation of the identified key regulatory factors converted all three colon cancer cell lines to a state similar to the transcriptome expression of normal colon tissues. >
Professor Kwang-Hyun Cho remarked, "The fact that cancer cells can be converted back to normal cells is an astonishing phenomenon. This study proves that such reversion can be systematically induced."
He further emphasized, "This research introduces the novel concept of reversible cancer therapy by reverting cancer cells to normal cells. It also develops foundational technology for identifying targets for cancer reversion through the systematic analysis of normal cell differentiation trajectories."
This research included contributions from Jeong-Ryeol Gong, Chun-Kyung Lee, Hoon-Min Kim, Juhee Kim, and Jaeog Jeon, and was published in the online edition of the international journal Advanced Science by Wiley on December 11. (Title: “Control of Cellular Differentiation Trajectories for Cancer Reversion”) DOI: https://doi.org/10.1002/advs.202402132
< Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed a source technology to systematically discover key control factors that can induce reversibility of colon cancer cells through a systems biology approach and a digital twin simulation analysis of the differentiation trajectory of normal colon cells, and verified the effects of reversion on actual colon cancer through molecular cell experiments and animal experiments. >
The study was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Mid-Career Researcher Program and Basic Research Laboratory Program. The research findings have been transferred to BioRevert Inc., where they will be used for the development of practical cancer reversion therapies.
2024.12.23 View 81489 -
KAIST Proposes a New Way to Circumvent a Long-time Frustration in Neural Computing
The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.
KAIST (President Kwang-Hyung Lee) announced on the 16th of December that Professor Se-Bum Paik 's research team in the Department of Brain Cognitive Sciences solved the weight transport problem*, a long-standing challenge in neural network learning, and through this, explained the principles that enable resource-efficient learning in biological brain neural networks.
*Weight transport problem: This is the biggest obstacle to the development of artificial intelligence that mimics the biological brain. It is the fundamental reason why large-scale memory and computational work are required in the learning of general artificial neural networks, unlike biological brains.
Over the past several decades, the development of artificial intelligence has been based on error backpropagation learning proposed by Geoffery Hinton, who won the Nobel Prize in Physics this year. However, error backpropagation learning was thought to be impossible in biological brains because it requires the unrealistic assumption that individual neurons must know all the connected information across multiple layers in order to calculate the error signal for learning.
< Figure 1. Illustration depicting the method of random noise training and its effects >
This difficult problem, called the weight transport problem, was raised by Francis Crick, who won the Nobel Prize in Physiology or Medicine for the discovery of the structure of DNA, after the error backpropagation learning was proposed by Hinton in 1986. Since then, it has been considered the reason why the operating principles of natural neural networks and artificial neural networks will forever be fundamentally different.
At the borderline of artificial intelligence and neuroscience, researchers including Hinton have continued to attempt to create biologically plausible models that can implement the learning principles of the brain by solving the weight transport problem.
In 2016, a joint research team from Oxford University and DeepMind in the UK first proposed the concept of error backpropagation learning being possible without weight transport, drawing attention from the academic world. However, biologically plausible error backpropagation learning without weight transport was inefficient, with slow learning speeds and low accuracy, making it difficult to apply in reality.
KAIST research team noted that the biological brain begins learning through internal spontaneous random neural activity even before experiencing external sensory experiences. To mimic this, the research team pre-trained a biologically plausible neural network without weight transport with meaningless random information (random noise).
As a result, they showed that the symmetry of the forward and backward neural cell connections of the neural network, which is an essential condition for error backpropagation learning, can be created. In other words, learning without weight transport is possible through random pre-training.
< Figure 2. Illustration depicting the meta-learning effect of random noise training >
The research team revealed that learning random information before learning actual data has the property of meta-learning, which is ‘learning how to learn.’ It was shown that neural networks that pre-learned random noise perform much faster and more accurate learning when exposed to actual data, and can achieve high learning efficiency without weight transport.
< Figure 3. Illustration depicting research on understanding the brain's operating principles through artificial neural networks >
Professor Se-Bum Paik said, “It breaks the conventional understanding of existing machine learning that only data learning is important, and provides a new perspective that focuses on the neuroscience principles of creating appropriate conditions before learning,” and added, “It is significant in that it solves important problems in artificial neural network learning through clues from developmental neuroscience, and at the same time provides insight into the brain’s learning principles through artificial neural network models.”
This study, in which Jeonghwan Cheon, a Master’s candidate of KAIST Department of Brain and Cognitive Sciences participated as the first author and Professor Sang Wan Lee of the same department as a co-author, was presented at the 38th Neural Information Processing Systems (NeurIPS), the world's top artificial intelligence conference, on December 14th in Vancouver, Canada. (Paper title: Pretraining with random noise for fast and robust learning without weight transport)
This study was conducted with the support of the National Research Foundation of Korea's Basic Research Program in Science and Engineering, the Information and Communications Technology Planning and Evaluation Institute's Talent Development Program, and the KAIST Singularity Professor Program.
2024.12.16 View 4276 -
KAIST Scientifically Identifies a Method to Prevent Dental Erosion from Carbonated Drinks
A Korean research team, which had previously visualized and scientifically proven the harmful effects of carbonated drinks like cola on dental health using nanotechnology, has now identified a mechanism for an effective method to prevent tooth damage caused by these beverages.
KAIST (represented by President Kwang Hyung Lee) announced on the 5th of December that a team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with Seoul National University's School of Dentistry (Departments of Pediatric Dentistry and Oral Microbiology) and Professor Hye Ryung Byon’s research team from the Department of Chemistry, has revealed through nanotechnology that silver diamine fluoride (SDF)* forms a fluoride-containing protective layer on the tooth surface, effectively inhibiting cola-induced erosion.
*SDF (Silver Diamine Fluoride): A dental agent primarily used for the treatment and prevention of tooth decay. SDF strengthens carious lesions, suppresses bacterial growth, and halts the progression of cavities.
The team analyzed the surface morphology and mechanical properties of tooth enamel on a nanoscale using atomic force microscopy (AFM). They also examined the chemical properties of the nano-film formed by SDF treatment using X-ray photoelectron spectroscopy (XPS)* and Fourier-transform infrared spectroscopy (FTIR)*.
*XPS (X-ray Photoelectron Spectroscopy): A powerful surface analysis technique used to investigate the chemical composition and electronic structure of materials.
*FTIR (Fourier-Transform Infrared Spectroscopy): An analytical method that identifies the molecular structure and composition of materials by analyzing how they absorb or transmit infrared light.
The findings showed significant differences in surface roughness and elastic modulus between teeth exposed to cola with and without SDF treatment. Teeth treated with SDF exhibited minimal changes in surface roughness due to erosion (from 64 nm to 70 nm) and maintained a high elastic modulus (from 215 GPa to 205 GPa).
This was attributed to the formation of a fluoroapatite* layer by SDF, which acted as a protective shield.
*Fluoroapatite: A phosphate mineral with the chemical formula Ca₅(PO₄)₃F (calcium fluoro-phosphate). It can occur naturally or be synthesized biologically/artificially and plays a crucial role in strengthening teeth and bones.
< Figure 1. Schematic of the workflow. Surface morphology and mechanical properties of untreated and treated silver diamine fluoride (SDF) treated enamel exposed to cola were analyzed over time using atomic force microscopy (AFM). >
Professor Young J. Kim from Seoul National University's Department of Pediatric Dentistry noted, "This technology could be applied to prevent dental erosion and strengthen teeth for both children and adults. It is a cost-effective and accessible dental treatment."
< Figure 2. Changes in surface roughness and elastic modulus according to time of exposure to cola for SDF untreated and treated teeth. After 1 hour, the surface roughness of the SDF untreated teeth rapidly became rougher from 83 nm to 287 nm and the elastic modulus weakened from 125 GPa to 13 GPa, whereas the surface roughness of the SDF treated teeth changed only slightly from 64 nm to 70 nm and the elastic modulus barely changed from 215 GPa to 205 GPa, maintaining a similar state to the initial state. >
Professor Seungbum Hong emphasized, "Dental health significantly impacts quality of life. This research offers an effective non-invasive method to prevent early dental erosion, moving beyond traditional surgical treatments. By simply applying SDF, dental erosion can be prevented and enamel strengthened, potentially reducing pain and costs associated with treatment."
This study, led by the first author Aditi Saha, a PhD student in KAIST’s Department of Materials Science and Engineering, was published in the international journal Biomaterials Research on November 7 under the title "Nanoscale Study on Noninvasive Prevention of Dental Erosion of Enamel by Silver Diamine Fluoride". The research was supported by the National Research Foundation of Korea.
2024.12.11 View 2580 -
KAIST Awarded Presidential Commendation for Contributions in Software Industry
- At the “25th Software Industry Day” celebration held in the afternoon on Monday, December 2nd, 2024 at Yangjae L Tower in Seoul
- KAIST was awarded the “Presidential Commendation” for its contributions for the advancement of the Software Industry in the Group Category
- Korea’s first AI master’s and doctoral degree program opened at KAIST Kim Jaechul Graduate School of AI
- Focus on training non-major developers through SW Officer Training Academy "Jungle", Machine Learning Engineer Bootcamp, etc., talents who can integrate development and collaboration, and advanced talents in the latest AI technologies.
- Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI received Prime Minister’s Commendation for his contributions for the advancement of the software industry.
< Photo 1. Professor Kyung-soo Kim, the Senior Vice President for Planning and Budget (second from the left) and the Manager of Planning Team, Mr. Sunghoon Jung, stand at the stage after receiving the Presidential Commendation as KAIST was selected as one of the groups that contributed to the advancement of the software industry at the "25th Software Industry Day" celebration. >
“KAIST has been leading the way in achieving the grand goal of fostering 1 million AI talents in Korea by services that pan from providing various educational opportunities, from developing the capabilities of experts with no computer science specialty to fostering advanced professionals. I would like to thank all members of KAIST community who worked hard to achieve the great feat of receiving the Presidential Commendations.” (KAIST President Kwang Hyung Lee)
KAIST (President Kwang Hyung Lee) announced on December 3rd that it was selected as a group that contributed to the advancement of the software industry at the “2024 Software Industry Day” celebration held at the Yangjae El Tower in Seoul on the 2nd of December and received a presidential commendation.
The “Software Industry Day”, hosted by the Ministry of Science and ICT and organized by the National IT Industry Promotion Agency and the Korea Software Industry Association, is an event designed to promote the status of software industry workers in Korea and to honor their achievements.
Every year, those who have made significant contributions to policy development, human resource development, and export growth for industry revitalization are selected and awarded the ‘Software Industry Development Contribution Award.’
KAIST was recognized for its contribution to developing a demand-based, industrial field-centric curriculum and fostering non-major developers and convergence talents with the goal of expanding software value and fostering excellent human resources.
< Photo 2. Senior Vice President for Planning and Budget Kyung-soo Kim receiving the commendation as the representative of KAIST >
Specifically, it first opened the SW Officer Training Academy "Jungle" to foster convergent program developers equipped with the abilities to handle both the computer coding and human interactions for collaborations. This is a non-degree program that provides intensive study and assignments for 5 months for graduates and intellectuals without prior knowledge of computer science.
KAIST Kim Jaechul Graduate School of AI opened and operated Korea’s first master's and doctoral degree program in the field of artificial intelligence. In addition, it planned a “Machine Learning Engineers’ Boot Camp” and conducted lectures and practical training for a total of 16 weeks on the latest AI technologies such as deep learning basics and large language models. It aims to strengthen the practical capabilities of start-up companies while lowering the threshold for companies to introduce AI technology.
Also, KAIST was selected to participate in the 1st and 2nd stages of the Software-centered University Project and has been taking part in the project since 2016. Through this, it was highly evaluated for promoting curriculum based on latest technology, an autonomous system where students directly select integrated education, and expansion of internships.
< Photo 3. Professor Minjoon Seo of Kim Jaechul Graduate School of AI, who received the Prime Minister's Commendation for his contribution to the advancement of the software industry on the same day >
At the awards ceremony that day, Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI also received the Prime Minister's Commendation for his contribution to the advancement of the software industry. Professor Seo was recognized for his leading research achievements in the fields of AI and natural language processing by publishing 28 papers in top international AI conferences over the past four years.
At the same time, he was noted for his contributions to enhancing the originality and innovation of language model research, such as △knowledge encoding, △knowledge access and utilization, and △high-dimensional inference performance, and for demonstrating leadership in the international academic community.
President Kwang Hyung Lee of KAIST stated, “Our university will continue to do its best to foster software talents with global competitiveness through continuous development of cutting-edge curriculum and innovative degree systems.”
2024.12.03 View 3146 -
KAIST Secures Core Technology for Ultra-High-Resolution Image Sensors
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.
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of November that a 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.
2024.11.22 View 2923 -
KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model
- Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices
The close relationship between AI and high-performance scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for their AI-related research contributions in their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel AI approach.
KAIST (President Kwang-Hyung Lee) announced on the 30th of October that Professor Yong-Hoon Kim's team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.
< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >
The quantum mechanical density functional theory (DFT) calculations using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow fast and accurate prediction of material properties.
*Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.
However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms.
*Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.
Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.
< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >
The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.
They then adopted a dataset of organic molecules with various chemical bonding characteristics, and applied random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.
< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >
Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin materials simulations across all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”
Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)
This research was conducted with support from the KAIST High-Risk Research Program for Graduate Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 3406 -
Team KAIST Crowned Champion for their World’s Best Ironman, Winning their 2nd Consecutive Win at the Cyborg Olympics
< Group photo of our research team participating in the 3rd Cybathlon >
The Cybathlon is an international competition, which aims to help overcome disabilities through the use of robotics technology. KAIST researchers came in third, winning the bronze medal, at their 1st participation in 2016, won their first gold medal at the 2nd competition in 2020, and took back the gold medal at this year’s 3rd competition, successfully defending their champion title.
KAIST (President Kwang-Hyung Lee) announced on the 28th of October that the wearable robot ‘WalkON Suit F1’ developed by the joint research team of KAIST EXO-Lab, Move Lab, and Angel Robotics, led by Professor Kyoungchul Kong of the Department of Mechanical Engineering (Chairman of Angel Robotics), won gold medal in Cybathlon 2024 held on the 27th.
< Seunghwan Kim, the Paraplegic Pilot of Team KAIST, cheering after completing all the missions >
Cybathlon is an international competition first held in Switzerland that challenges teams to develop assistive technologies with the purpose of overcoming disabilities, and is also called the Cyborg Olympics. Right after each competition, the missions for the next competition are announced, and research teams from around the world research and develop the right assistive technology for next four years to accomplish the given mission.
Aside from the Exoskeleton Race, competitions in a total of eight disciplines are held, including Arm Prosthesis Race, Leg Prosthesis Race, and Wheelchair Race. A total of 71 teams from 26 countries participated in the 3rd Cybathlon event. Professor Kyoungchul Kong’s research team participated in the Exoskeleton Race, the same discipline they took part in the previous competition.
The Exoskeleton Race is the highlight of the event that can be called the core of the Cybathlon. In the prosthetic arm or leg events, disabled athletes wearing traditional assistive devices instead of powered devices often win depending more on the ability of the athletes rather than the technology behind the tools.
However, the exoskeleton event requires individuals with paraplegic disability to walk completely dependent on the robotic device to perform various missions, so the technical difficulty and the dependence on robotics technology is high.
In fact, many teams gave up on participating after seeing the missions for this competition, and more than half of the research teams declared withdrawal during the developmental process. In the end, only six teams from Korea, Thailands, Switzerland, Germany, and the Netherlands participated in the actual competition. Even the research team from the Swiss headquarters declared forfeiture midway as the competition date drew nearer.
< Cybathlon 2024 – Exoskeleton Race Mission Description >
The reason why many teams gave up in the exoskeleton discipline was because the difficulty of the missions was unusually high. Most research teams have the skills to make paraplegic athletes walk, but there were many other difficult tasks, such as making them walk without crutches or using both hands, while standing free on both legs on the exoskeleton, to cut a lump of sponge block, as in imitating food preparing process.
The reason why the difficulty of the missions increased like this is because Professor Kong's research team completed all the missions given to them too quickly in the last competition. In fact, in the last competition, there was even a question asked whether Kim Byeong-Uk (paraplegic) wearing the WalkON Suit F1 was really disabled.
Professor Kong's research team developed WalkON Suit F1 to successfully complete the missions. The number of motorized joints increased from six to twelve, and the output of the motor itself was more than doubled compared to the previous model that ran in the competition back in 2020. The 6-channel ground reaction force sensor on the foot measured the robot's balance 1,000 times per second to maintain balance. Cameras were installed to detect obstacles, and an AI board for implementation of AI neural network was also installed.
On top of the technologies required to complete the competition missions, a function was implemented that allows the users to wear the robot by themselves and dock on to it right from their wheelchair. In the process, all parts were domestically produced and all basic technologies were internalized. The outer design of the robot was done by Professor Hyunjoon Park of KAIST, and the harmony between people and robots was pursued.
In the end, the results of the competition were as expected. The only team that could successfully perform all of the mission tasks, which were originally designed to challenge Professor Kong's research team, was Professor Kong's team. They successfully completed missions such as moving by sidesteps between narrow chairs, moving boxes, walking freely unassisted by crutches, passing through a narrow door and closing it behind, and working on food preparation in the kitchen, recording 6 minutes and 41 seconds to complete all six tasks.
The Swiss and Thai teams that took 2nd and 3rd places were all given 10 minutes, but only were able to perform two missions, each earning twenty points. It was an unevenly matched race to begin with. The Cybathlon broadcast team was more surprised and interested in the performance of WalkON Suit F1 than in result of the race.
< Team KAIST’s Paraplegic Pilot Seunghwan Kim (left), and Professor Kyoungchul Kong (right) >
Researcher Jeongsu Park, the captain of Team KAIST, said, “We came into this competition thinking of it as a competition against ourselves to begin with and focused on showing the technological gap. Now, we are very happy and proud that our endeavors achieved such good result as well.” He added, “We plan to continue to showcase various functions of the WalkON Suit F1 that have not yet been publicly introduced.”
Researcher Seunghwan Kim, the paraplegic athlete of the team, said, “I am so touched that I was able to introduce the world’s best wearable robot technology of Korea with my own body.”
On a different note, Professor Kong’s research team has successfully commercialized wearable robots through Angel Robotics Co., Ltd. since the 2020 competition. In 2022, they began distributing “ANGEL LEGS M20,” the first wearable robot to be covered by health insurance, and as a result, Angel Robotics Co., Ltd. was successfully listed on KOSDAQ this March.
The various know-how and core technologies accumulated while preparing for this competition is to contribute to further development and propagation of wearable robots, provoking imagination to draw on the future of wearable robots and on how it may change our daily lives.
Final Match (Self-filmed): https://youtu.be/3ASAtvkiOhw
Final Match and Interview (Official Video): https://youtu.be/FSfxOTpDjSE
Final Match and Interview (Summary): https://youtu.be/Sb_vd5-3f_0
2024.10.28 View 5930 -
KAIST Professor Uichin Lee Receives Distinguished Paper Award from ACM
< Photo. Professor Uichin Lee (left) receiving the award >
KAIST (President Kwang Hyung Lee) announced on the 25th of October that Professor Uichin Lee’s research team from the School of Computing received the Distinguished Paper Award at the International Joint Conference on Pervasive and Ubiquitous Computing and International Symposium on Wearable Computing (Ubicomp / ISWC) hosted by the Association for Computing Machinery (ACM) in Melbourne, Australia on October 8.
The ACM Ubiquitous Computing Conference is the most prestigious international conference where leading universities and global companies from around the world present the latest research results on ubiquitous computing and wearable technologies in the field of human-computer interaction (HCI).
The main conference program is composed of invited papers published in the Proceedings of the ACM (PACM) on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), which covers the latest research in the field of ubiquitous and wearable computing.
The Distinguished Paper Award Selection Committee selected eight papers among 205 papers published in Vol. 7 of the ACM Proceedings (PACM IMWUT) that made outstanding and exemplary contributions to the research community. The committee consists of 16 prominent experts who are current and former members of the journal's editorial board which made the selection after a rigorous review of all papers for a period that stretched over a month.
< Figure 1. BeActive mobile app to promote physical activity to form active lifestyle habits >
The research that won the Distinguished Paper Award was conducted by Dr. Junyoung Park, a graduate of the KAIST Graduate School of Data Science, as the 1st author, and was titled “Understanding Disengagement in Just-in-Time Mobile Health Interventions”
Professor Uichin Lee’s research team explored user engagement of ‘Just-in-Time Mobile Health Interventions’ that actively provide interventions in opportune situations by utilizing sensor data collected from health management apps, based on the premise that these apps are aptly in use to ensure effectiveness.
< Figure 2. Traditional user-requested digital behavior change intervention (DBCI) delivery (Pull) vs. Automatic transmission (Push) for Just-in-Time (JIT) mobile DBCI using smartphone sensing technologies >
The research team conducted a systematic analysis of user disengagement or the decline in user engagement in digital behavior change interventions. They developed the BeActive system, an app that promotes physical activities designed to help forming active lifestyle habits, and systematically analyzed the effects of users’ self-control ability and boredom-proneness on compliance with behavioral interventions over time.
The results of an 8-week field trial revealed that even if just-in-time interventions are provided according to the user’s situation, it is impossible to avoid a decline in participation. However, for users with high self-control and low boredom tendency, the compliance with just-in-time interventions delivered through the app was significantly higher than that of users in other groups.
In particular, users with high boredom proneness easily got tired of the repeated push interventions, and their compliance with the app decreased more quickly than in other groups.
< Figure 3. Just-in-time Mobile Health Intervention: a demonstrative case of the BeActive system: When a user is identified to be sitting for more than 50 mins, an automatic push notification is sent to recommend a short active break to complete for reward points. >
Professor Uichin Lee explained, “As the first study on user engagement in digital therapeutics and wellness services utilizing mobile just-in-time health interventions, this research provides a foundation for exploring ways to empower user engagement.” He further added, “By leveraging large language models (LLMs) and comprehensive context-aware technologies, it will be possible to develop user-centered AI technologies that can significantly boost engagement."
< Figure 4. A conceptual illustration of user engagement in digital health apps. Engagement in digital health apps consists of (1) engagement in using digital health apps and (2) engagement in behavioral interventions provided by digital health apps, i.e., compliance with behavioral interventions. Repeated adherences to behavioral interventions recommended by digital health apps can help achieve the distal health goals. >
This study was conducted with the support of the 2021 Biomedical Technology Development Program and the 2022 Basic Research and Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.
< Figure 5. A conceptual illustration of user disengagement and engagement of digital behavior change intervention (DBCI) apps. In general, user engagement of digital health intervention apps consists of two components: engagement in digital health apps and engagement in behavioral interventions recommended by such apps (known as behavioral compliance or intervention adherence). The distinctive stages of user can be divided into adoption, abandonment, and attrition. >
< Figure 6. Trends of changes in frequency of app usage and adherence to behavioral intervention over 8 weeks, ● SC: Self-Control Ability (High-SC: user group with high self-control, Low-SC: user group with low self-control) ● BD: Boredom-Proneness (High-BD: user group with high boredom-proneness, Low-BD: user group with low boredom-proneness). The app usage frequencies were declined over time, but the adherence rates of those participants with High-SC and Low-BD were significantly higher than other groups. >
2024.10.25 View 4469 -
KAIST Introduces a Wearable Robot that Walks and Puts itself on to Disabled Persons
< Photo 1. WalkON Suit F1 - walking demonstration >
KAIST researchers have unveiled a new wearable robot developed for completely paralyzed persons that can walk to them so that the user can wear it right out of their wheelchairs without the help from others. Also, it was announced that Professor Kyoungchul Kong's team from KAIST will be participating in the wearable robot category of the 3rd Cybathlon, which is being held four years after the team’s gold medal win in 2020.
KAIST (President Kwang-Hyung Lee) announced this new version of the wearable robot for paraplegic people, WalkON Suit F1, by Professor Kyoungchul Kong (CEO and founder of Angel Robotics) of KAIST Department of Mechanical Engineering on the 24th of October.
< Photo 2. (From left) Professor Kyoungchul Kong of the Department of Mechanical Engineering, Researcher Seunghwan Kim (the competing athlete), and PhD candidate Jeongsu Park (the leader of Team KAIST) >
WalkON Suit is a wearable robot for people suffering with paraplegic disabilities that the research team has been continuously researching since 2015. This robot targets to assist persons with American Spinal Injury Association (ASIA) Impairment Scale – A (complete paralysis) grade injury, the most severe level of paraplegia. Therefore, its development purpose is different from that of other rehabilitation therapy and muscle strength assisting robots currently being supplied nationally by Angel Robotics.
Professor Kong's research team first announced WalkON Suit 1 in 2016, and then introduced WalkON Suit 4 in 2020, increasing the walking speed to 3.2 km/h, achieving the normal walking speed of people with no disabilities. In addition, it demonstrated the ability to pass through obstacles such as narrow passages, doors, and stairs that can be encountered in daily life.
However, it had the same fundamental problem all wearable robots have, which is that they require the help of others to wear the robot. While you can walk without help from others once you are wearing the robot, you needed someone's help to put it on to begin with.
The newly released WalkON Suit F1 presented a technical solution to this fundamental problem. It applied a frontal-docking method instead of a rear-sitting method so that you can wear the robot right away without getting out of the wheelchair and into the robot, which would require help from others mid-transition.
< Photo 3. WalkON Suit F1 - suiting-up demonstration >
In addition, before wearing the robot, it can walk on its own like a humanoid robot and approaches the user. It is also implemented a function that actively controls the center of its weight against the pull of gravity so that it maintains balance without falling over even if the user pushes the robot otherwise. The outer design of the WalkON Suit F1, which crosses between a humanoid and a wearable robot, was done by Professor Hyunjoon Park of the Department of Industrial Design at KAIST.
The original function of the wearable robot has also been greatly improved. The performance of the balance control was improved to allow the free use of both hands in upright state, as well as the freedom to take several steps without a cane.
Technological advancements at the components level are also noteworthy. Through close collaboration with Angel Robotics, all core components of the robot, such as the motor, reducer, motor driver, and main circuit, have been domestically produced. The output density of the motor and reducer modules has been improved by about two folds (based on power per weight) compared to the research team's existing technology, and the control performance of the motor driver has been improved by about three times (based on frequency response speed) compared to the best overseas technology.
In particular, the embedded software technology of the motor driver has been significantly improved so that advanced motion control algorithms can be stably implemented without using expensive higher-level controllers. In addition, visual recognition system for obstacle detection and an AI board for neural network application have been installed.
< Figure 1. WalkON Suit F1 shape and main specifications >
Professor Kong explained, “WalkON Suit is the culmination of wearable robot technology for the disabled,” and added, “The numerous components, control, and module technologies derived from WalkON Suit are setting the standard for the entire wearable robot industry.”
Professor Kong’s research team revealed WalkON Suit F1 and announced that they will be participating in the 3rd Cybathlon, which is being held after four years since the last event. In this competition, which will be held on October 27, Professor Kong’s lab, the Exo Lab will be participating with Jeongsu Park, a Ph.D. Student, as the leader and Seunghwan Kim, the lab’s staff researcher with complete paralysis, as the competing athlete.
The difficulty of the missions in this competition has been significantly increased compared to the previous competition, and the number of missions has increased from six to ten. Some missions have been criticized for being overly challenging, going beyond the level that can be encountered in everyday life.
< Photo 4. Cybathlon stadium (Angel Robotics Asia Hub) >
Regarding this, the team leader Jeongsu Park expressed his ambition, saying, “Since we already won first place in the previous competition, our goal in this competition is to show the technological gap rather than competing for rankings.”
The Cybathlon is a cyborg Olympics held every four years in Switzerland. This competition will be held in a hybrid format, with some participants taking part in Switzerland while others broadcasting live from stadiums in their own country on October 27. Professor Kong's research team will be participating via live broadcast from the competition facilities installed in Angel Robotics' Advanced Research Institute (Planet Daejeon).
< Photo 5. Photo of Team KAIST participating in Cybathlon 2024 >
The demonstration video of WalkON Suit F1 can be viewed through the link below.
Link: https://www.youtube.com/@KyoungchulKong_EXO-Lab
2024.10.24 View 8744 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 3785