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.
〈(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.
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 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.
<(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 traditional 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.
<(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 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.
Prof. John Kim has been elevated to IEEE Fellow, class of 2025, “for contributions to the design and analysis of high-performance interconnection network architectures.” Prof. Kim’s research area is in computer architecture and interconnection networks. As systems scale-up and scale-out with more components, data movement is becoming a bigger bottleneck in modern digital systems. Prof. Kim’s research addresses the communication bottleneck in multi-core and large-scale systems and his research lab is also currently exploring communication bottlenecks in deep-learning systems.
Prof. Kim’s research has led him to become the first researcher from Asia to be inducted into the hall-of-fame for all three main computer architecture conferences — ISCA, MICRO, and HPCA. He was also the first researcher from an Asia institution to serve as the program chair for a top-tier computer architecture conference (HPCA’24). Prof. Kim plans on pursuing research on efficient data movement across memory-centric architectures and exploiting domain-specific networks.
< Photo. The research team of the School of Electrical Engineering posed by the newly deveoped processor. (From center to the right) Professor Young-Gyu Yoon, Integrated Master’s and Doctoral Program Students Seungjae Han and Hakcheon Jeong and Professor Shinhyun Choi >
Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. EE research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.
The joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own.
< Figure 1. Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32×32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). >
What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.
This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team’s main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components.
The research team has developed the world’s first memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology.
< Figure 2. Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by our research team (right). >
At the heart of this innovation is a next-generation semiconductor device called a memristor*. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilizing it, data storage and computation can be performed simultaneously, just like our brain cells.*Memristor: A compound word of memory and resistor, next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past.
The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialization possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference.
This technology will revolutionize the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected, and more energy-efficient.
“This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” explained KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.”
The research was conducted with Hakcheon Jeong and Seungjae Han, the students of Integrated Master’s and Doctoral Program at KAIST School of Electrical Engineering being the co-first authors, the results of which was published online in the international academic journal, Nature Electronics, on January 8, 2025. *Paper title: Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array ( https://doi.org/10.1038/s41928-024-01318-6 )
This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Project, Excellent New Researcher Project and PIM AI Semiconductor Core Technology Development Project of the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute Research and Development Support Project of the Institute of Information & communications Technology Planning & Evaluation.
< Photo 1. (From top left) Professor Hyunjoo J. Lee, Dr. Mi-Young Son, Dr. Mi-Ok Lee (In the front row from left) Doctoral student Kiup Kim, Doctoral student Youngsun Lee >
The EE research team led by Professor Hyunjoo J. Lee in collaboration with Dr. Mi-Young Son and Dr. Mi-Ok Lee at Korea Research Institute of and Biotechnology (KRIBB has developed a highly stretchable protruding microelectrode array platform for non-invasive electrophysiological signal measurement of organoids.
Organoids* are highly promising models for human biology and are expected to replace many animal experiments. Their potential applications include disease modeling, drug screening, and personalized medicine as they closely mimic the structure and function of humans. *Organoids: three-dimensional in vitro tissue models derived from human stem cells
Despite these advantages, existing organoid research has primarily focused on genetic analysis, with limited studies on organoid functionality. For effective drug evaluation and precise biological research, technology that preserves the three-dimensional structure of organoids while enabling real-time monitoring of their functions is needed. However, it’s challenging to provide non-invasive ways to evaluate the functionalities without incurring damage to the tissues. This challenge is particularly significant for electrophysiological signal measurement in cardiac and brain organoids since the sensor needs to be in direct contact with organoids of varying size and irregular shape. Achieving tight contact between electrodes and the external surface of the organoids without damaging the organoids has been a persistent challenge.
< Figure 1. Schematic image of highly stretchable MEA (sMEA) with protruding microelectrodes. >
Prof. Hyunjoo J. Lee’s research team developed a highly stretchable microelectrode array with a unique serpentine structure that contacts the surface of organoids in a highly conformal fashion. They successfully demonstrated real-time measurement and analysis of electrophysiological signals from two types of electrogenic organoids (heart and brain). By employing a micro-electromechanical system (MEMS)-based process, the team fabricated the serpentine-structured microelectrode array and used an electrochemical deposition process to develop PEDOT:PSS-based protruding microelectrodes. These innovations demonstrated exceptional stretchability and close surface adherence to various organoid sizes. The protruding microelectrodes improved contact between organoids and the electrodes, ensuring stable and reliable electrophysiological signal measurements with high signal-to-noise ratios (SNR).
< Figure 2. Conceptual illustration, optical image, and fluorescence images of an organoid captured by the sMEA with protruding microelectrodes.>
Using this technology, the team successfully monitored and analyzed electrophysiological signals from cardiac spheroids of various sizes, revealing three-dimensional signal propagation patterns and identifying changes in signal characteristics according to size. They also measured electrophysiological signals in midbrain organoids, demonstrating the versatility of the technology. Additionally, they monitored signal modulations induced by various drugs, showcasing the potential of this technology for drug screening applications.
Prof. Hyunjoo Jenny Lee stated, “By integrating MEMS technology and electrochemical deposition techniques, we successfully developed a stretchable microelectrode array adaptable to organoids of diverse sizes and shapes. The high practicality is a major advantage of this system since the fabrication is based on semiconductor fabrication with high volume production, reliability, and accuracy. This technology that enables in situ, real-time analysis of states and functionalities of organoids will be a game changer in high-through drug screening.”
This study led by Ph.D. candidate Kiup Kim from KAIST and Ph.D. candidate Youngsun Lee from KRIBB, with significant contributions from Dr. Kwang Bo Jung, was published online on December 15, 2024 in Advanced Materials (IF: 27.4).
< Figure 4. Drug screening using cardiac spheroids and midbrain organoids.>
This research was supported by a grant from 3D-TissueChip Based Drug Discovery Platform Technology Development Program (No. 20009209) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), by the Commercialization Promotion Agency for R&D Outcomes (COMPA) funded by the Ministry of Science and ICT (MSIT) (RS-2024-00415902), by the K-Brain Project of the National Research Foundation (NRF) funded by the Korean government (MSIT) (RS-2023-00262568), by BK21 FOUR (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE, No. 4120200113769), and by Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program (KGM4722432).
<Prof. John Kim>
