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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 102 -
A 20-year-old puzzle solved: KAIST research team reveals the 'three-dimensional vortex' of zero-dimensional ferroelectrics
Materials that can maintain a magnetized state by themselves without an external magnetic field (i.e., permanent magnets) are called ferromagnets. Ferroelectrics can be thought of as the electric counterpart to ferromagnets, as they maintain a polarized state without an external electric field. It is well-known that ferromagnets lose their magnetic properties when reduced to nano sizes below a certain threshold. What happens when ferroelectrics are similarly made extremely small in all directions (i.e., into a zero-dimensional structure such as nanoparticles) has been a topic of controversy for a long time.
< (From left) Professor Yongsoo Yang, the corresponding author, and Chaehwa Jeong, the first author studying in the integrated master’s and doctoral program, of the KAIST Department of Physics >
The research team led by Dr. Yongsoo Yang from the Department of Physics at KAIST has, for the first time, experimentally clarified the three-dimensional, vortex-shaped polarization distribution inside ferroelectric nanoparticles through international collaborative research with POSTECH, SNU, KBSI, LBNL and University of Arkansas.
About 20 years ago, Prof. Laurent Bellaiche (currently at University of Arkansas) and his colleagues theoretically predicted that a unique form of polarization distribution, arranged in a toroidal vortex shape, could occur inside ferroelectric nanodots. They also suggested that if this vortex distribution could be properly controlled, it could be applied to ultra-high-density memory devices with capacities over 10,000 times greater than existing ones. However, experimental clarification had not been achieved due to the difficulty of measuring the three-dimensional polarization distribution within ferroelectric nanostructures.
The research team at KAIST successfully solved this 20-year-old challenge by implementing a technique called atomic electron tomography. This technique works by acquiring atomic-resolution transmission electron microscope images of the nanomaterials from multiple tilt angles, and then reconstructing them back into three-dimensional structures using advanced reconstruction algorithms. Electron tomography can be understood as essentially the same method with the CT scans used in hospitals to view internal organs in three dimensions; the KAIST team adapted it uniquely for nanomaterials, utilizing an electron microscope at the single-atom level.
< Figure 1. Three-dimensional polarization distribution of BaTiO3 nanoparticles revealed by atomic electron tomography. >(Left) Schematic of the electron tomography technique, which involves acquiring transmission electron microscope images at multiple tilt angles and reconstructing them into 3D atomic structures.(Center) Experimentally determined three-dimensional polarization distribution inside a BaTiO3 nanoparticle via atomic electron tomography. A vortex-like structure is clearly visible near the bottom (blue dot).(Right) A two-dimensional cross-section of the polarization distribution, thinly sliced at the center of the vortex, with the color and arrows together indicating the direction of the polarization. A distinct vortex structure can be observed.
Using atomic electron tomography, the team completely measured the positions of cation atoms inside barium titanate (BaTiO3) nanoparticles, a well-known ferroelectric material, in three dimensions. From the precisely determined 3D atomic arrangements, they were able to further calculate the internal three-dimensional polarization distribution at the single-atom level. The analysis of the polarization distribution revealed, for the first time experimentally, that topological polarization orderings including vortices, anti-vortices, skyrmions, and a Bloch point occur inside the 0-dimensional ferroelectrics, as theoretically predicted 20 years ago. Furthermore, it was also found that the number of internal vortices can be controlled depending on their sizes.
Prof. Sergey Prosandeev and Prof. Bellaiche (who proposed with other co-workers the polar vortex ordering theoretically 20 years ago), joined this collaboration and further proved that the vortex distribution results obtained from experiments are consistent with theoretical calculations.
By controlling the number and orientation of these polarization distributions, it is expected that this can be utilized into next-generation high-density memory device that can store more than 10,000 times the amount of information in the same-sized device compared to existing ones.
Dr. Yang, who led the research, explained the significance of the results: “This result suggests that controlling the size and shape of ferroelectrics alone, without needing to tune the substrate or surrounding environmental effects such as epitaxial strain, can manipulate ferroelectric vortices or other topological orderings at the nano-scale. Further research could then be applied to the development of next-generation ultra-high-density memory.”
This research, with Chaehwa Jeong from the Department of Physics at KAIST as the first author, was published online in Nature Communications on May 8th (Title: Revealing the Three-Dimensional Arrangement of Polar Topology in Nanoparticles).
The study was mainly supported by the National Research Foundation of Korea (NRF) Grants funded by the Korean Government (MSIT).
2024.05.31 View 3609 -
KAIST builds a high-resolution 3D holographic sensor using a single mask
Holographic cameras can provide more realistic images than ordinary cameras thanks to their ability to acquire 3D information about objects. However, existing holographic cameras use interferometers that measure the wavelength and refraction of light through the interference of light waves, which makes them complex and sensitive to their surrounding environment.
On August 23, a KAIST research team led by Professor YongKeun Park from the Department of Physics announced a new leap forward in 3D holographic imaging sensor technology.
The team proposed an innovative holographic camera technology that does not use complex interferometry. Instead, it uses a mask to precisely measure the phase information of light and reconstruct the 3D information of an object with higher accuracy.
< Figure 1. Structure and principle of the proposed holographic camera. The amplitude and phase information of light scattered from a holographic camera can be measured. >
The team used a mask that fulfills certain mathematical conditions and incorporated it into an ordinary camera, and the light scattered from a laser is measured through the mask and analyzed using a computer. This does not require a complex interferometer and allows the phase information of light to be collected through a simplified optical system. With this technique, the mask that is placed between the two lenses and behind an object plays an important role. The mask selectively filters specific parts of light,, and the intensity of the light passing through the lens can be measured using an ordinary commercial camera. This technique combines the image data received from the camera with the unique pattern received from the mask and reconstructs an object’s precise 3D information using an algorithm.
This method allows a high-resolution 3D image of an object to be captured in any position. In practical situations, one can construct a laser-based holographic 3D image sensor by adding a mask with a simple design to a general image sensor. This makes the design and construction of the optical system much easier. In particular, this novel technology can capture high-resolution holographic images of objects moving at high speeds, which widens its potential field of application.
< Figure 2. A moving doll captured by a conventional camera and the proposed holographic camera. When taking a picture without focusing on the object, only a blurred image of the doll can be obtained from a general camera, but the proposed holographic camera can restore the blurred image of the doll into a clear image. >
The results of this study, conducted by Dr. Jeonghun Oh from the KAIST Department of Physics as the first author, were published in Nature Communications on August 12 under the title, "Non-interferometric stand-alone single-shot holographic camera using reciprocal diffractive imaging".
Dr. Oh said, “The holographic camera module we are suggesting can be built by adding a filter to an ordinary camera, which would allow even non-experts to handle it easily in everyday life if it were to be commercialized.” He added, “In particular, it is a promising candidate with the potential to replace existing remote sensing technologies.”
This research was supported by the National Research Foundation’s Leader Research Project, the Korean Ministry of Science and ICT’s Core Hologram Technology Support Project, and the Nano and Material Technology Development Project.
2023.09.05 View 4279 -
Tomographic Measurement of Dielectric Tensors
Dielectric tensor tomography allows the direct measurement of the 3D dielectric tensors of optically anisotropic structures
A research team reported the direct measurement of dielectric tensors of anisotropic structures including the spatial variations of principal refractive indices and directors. The group also demonstrated quantitative tomographic measurements of various nematic liquid-crystal structures and their fast 3D nonequilibrium dynamics using a 3D label-free tomographic method. The method was described in Nature Materials.
Light-matter interactions are described by the dielectric tensor. Despite their importance in basic science and applications, it has not been possible to measure 3D dielectric tensors directly. The main challenge was due to the vectorial nature of light scattering from a 3D anisotropic structure. Previous approaches only addressed 3D anisotropic information indirectly and were limited to two-dimensional, qualitative, strict sample conditions or assumptions.
The research team developed a method enabling the tomographic reconstruction of 3D dielectric tensors without any preparation or assumptions. A sample is illuminated with a laser beam with various angles and circularly polarization states. Then, the light fields scattered from a sample are holographically measured and converted into vectorial diffraction components. Finally, by inversely solving a vectorial wave equation, the 3D dielectric tensor is reconstructed.
Professor YongKeun Park said, “There were a greater number of unknowns in direct measuring than with the conventional approach. We applied our approach to measure additional holographic images by slightly tilting the incident angle.”
He said that the slightly tilted illumination provides an additional orthogonal polarization, which makes the underdetermined problem become the determined problem. “Although scattered fields are dependent on the illumination angle, the Fourier differentiation theorem enables the extraction of the same dielectric tensor for the slightly tilted illumination,” Professor Park added.
His team’s method was validated by reconstructing well-known liquid crystal (LC) structures, including the twisted nematic, hybrid aligned nematic, radial, and bipolar configurations. Furthermore, the research team demonstrated the experimental measurements of the non-equilibrium dynamics of annihilating, nucleating, and merging LC droplets, and the LC polymer network with repeating 3D topological defects.
“This is the first experimental measurement of non-equilibrium dynamics and 3D topological defects in LC structures in a label-free manner. Our method enables the exploration of inaccessible nematic structures and interactions in non-equilibrium dynamics,” first author Dr. Seungwoo Shin explained.
-PublicationSeungwoo Shin, Jonghee Eun, Sang Seok Lee, Changjae Lee, Herve Hugonnet, Dong Ki Yoon, Shin-Hyun Kim, Jongwoo Jeong, YongKeun Park, “Tomographic Measurement ofDielectric Tensors at Optical Frequency,” Nature Materials March 02, 2022 (https://doi.org/10/1038/s41563-022-01202-8)
-ProfileProfessor YongKeun ParkBiomedical Optics Laboratory (http://bmol.kaist.ac.kr)Department of PhysicsCollege of Natural SciencesKAIST
2022.03.22 View 6158 -
AI Light-Field Camera Reads 3D Facial Expressions
Machine-learned, light-field camera reads facial expressions from high-contrast illumination invariant 3D facial images
A joint research team led by Professors Ki-Hun Jeong and Doheon Lee from the KAIST Department of Bio and Brain Engineering reported the development of a technique for facial expression detection by merging near-infrared light-field camera techniques with artificial intelligence (AI) technology.
Unlike a conventional camera, the light-field camera contains micro-lens arrays in front of the image sensor, which makes the camera small enough to fit into a smart phone, while allowing it to acquire the spatial and directional information of the light with a single shot. The technique has received attention as it can reconstruct images in a variety of ways including multi-views, refocusing, and 3D image acquisition, giving rise to many potential applications.
However, the optical crosstalk between shadows caused by external light sources in the environment and the micro-lens has limited existing light-field cameras from being able to provide accurate image contrast and 3D reconstruction.
The joint research team applied a vertical-cavity surface-emitting laser (VCSEL) in the near-IR range to stabilize the accuracy of 3D image reconstruction that previously depended on environmental light. When an external light source is shone on a face at 0-, 30-, and 60-degree angles, the light field camera reduces 54% of image reconstruction errors. Additionally, by inserting a light-absorbing layer for visible and near-IR wavelengths between the micro-lens arrays, the team could minimize optical crosstalk while increasing the image contrast by 2.1 times.
Through this technique, the team could overcome the limitations of existing light-field cameras and was able to develop their NIR-based light-field camera (NIR-LFC), optimized for the 3D image reconstruction of facial expressions. Using the NIR-LFC, the team acquired high-quality 3D reconstruction images of facial expressions expressing various emotions regardless of the lighting conditions of the surrounding environment.
The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy – a statistically significant figure compared to when 2D images were used. Furthermore, by calculating the interdependency of distance information that varies with facial expression in 3D images, the team could identify the information a light-field camera utilizes to distinguish human expressions.
Professor Ki-Hun Jeong said, “The sub-miniature light-field camera developed by the research team has the potential to become the new platform to quantitatively analyze the facial expressions and emotions of humans.” To highlight the significance of this research, he added, “It could be applied in various fields including mobile healthcare, field diagnosis, social cognition, and human-machine interactions.”
This research was published in Advanced Intelligent Systems online on December 16, under the title, “Machine-Learned Light-field Camera that Reads Facial Expression from High-Contrast and Illumination Invariant 3D Facial Images.” This research was funded by the Ministry of Science and ICT and the Ministry of Trade, Industry and Energy.
-Publication“Machine-learned light-field camera that reads fascial expression from high-contrast and illumination invariant 3D facial images,” Sang-In Bae, Sangyeon Lee, Jae-Myeong Kwon, Hyun-Kyung Kim. Kyung-Won Jang, Doheon Lee, Ki-Hun Jeong, Advanced Intelligent Systems, December 16, 2021 (doi.org/10.1002/aisy.202100182)
ProfileProfessor Ki-Hun JeongBiophotonic LaboratoryDepartment of Bio and Brain EngineeringKAIST
Professor Doheon LeeDepartment of Bio and Brain EngineeringKAIST
2022.01.21 View 9616 -
3D Visualization and Quantification of Bioplastic PHA in a Living Bacterial Cell
3D holographic microscopy leads to in-depth analysis of bacterial cells accumulating the bacterial bioplastic, polyhydroxyalkanoate (PHA)
A research team at KAIST has observed how bioplastic granule is being accumulated in living bacteria cells through 3D holographic microscopy. Their 3D imaging and quantitative analysis of the bioplastic ‘polyhydroxyalkanoate’ (PHA) via optical diffraction tomography provides insights into biosynthesizing sustainable substitutes for petroleum-based plastics.
The bio-degradable polyester polyhydroxyalkanoate (PHA) is being touted as an eco-friendly bioplastic to replace existing synthetic plastics. While carrying similar properties to general-purpose plastics such as polyethylene and polypropylene, PHA can be used in various industrial applications such as container packaging and disposable products.
PHA is synthesized by numerous bacteria as an energy and carbon storage material under unbalanced growth conditions in the presence of excess carbon sources. PHA exists in the form of insoluble granules in the cytoplasm. Previous studies on investigating in vivo PHA granules have been performed by using fluorescence microscopy, transmission electron microscopy (TEM), and electron cryotomography.
These techniques have generally relied on the statistical analysis of multiple 2D snapshots of fixed cells or the short-time monitoring of the cells. For the TEM analysis, cells need to be fixed and sectioned, and thus the investigation of living cells was not possible. Fluorescence-based techniques require fluorescence labeling or dye staining. Thus, indirect imaging with the use of reporter proteins cannot show the native state of PHAs or cells, and invasive exogenous dyes can affect the physiology and viability of the cells. Therefore, it was difficult to fully understand the formation of PHA granules in cells due to the technical limitations, and thus several mechanism models based on the observations have been only proposed.
The team of metabolic engineering researchers led by Distinguished Professor Sang Yup Lee and Physics Professor YongKeun Park, who established the startup Tomocube with his 3D holographic microscopy, reported the results of 3D quantitative label-free analysis of PHA granules in individual live bacterial cells by measuring the refractive index distributions using optical diffraction tomography. The formation and growth of PHA granules in the cells of Cupriavidus necator, the most-studied native PHA (specifically, poly(3-hydroxybutyrate), also known as PHB) producer, and recombinant Escherichia coli harboring C. necator PHB biosynthesis pathway were comparatively examined.
From the reconstructed 3D refractive index distribution of the cells, the team succeeded in the 3D visualization and quantitative analysis of cells and intracellular PHA granules at a single-cell level. In particular, the team newly presented the concept of “in vivo PHA granule density.” Through the statistical analysis of hundreds of single cells accumulating PHA granules, the distinctive differences of density and localization of PHA granules in the two micro-organisms were found. Furthermore, the team identified the key protein that plays a major role in making the difference that enabled the characteristics of PHA granules in the recombinant E. coli to become similar to those of C. necator.
The research team also presented 3D time-lapse movies showing the actual processes of PHA granule formation combined with cell growth and division. Movies showing the living cells synthesizing and accumulating PHA granules in their native state had never been reported before.
Professor Lee said, “This study provides insights into the morphological and physical characteristics of in vivo PHA as well as the unique mechanisms of PHA granule formation that undergo the phase transition from soluble monomers into the insoluble polymer, followed by granule formation. Through this study, a deeper understanding of PHA granule formation within the bacterial cells is now possible, which has great significance in that a convergence study of biology and physics was achieved. This study will help develop various bioplastics production processes in the future.”
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (Grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) and the Bio & Medical Technology Development Program (Grant No. 2021M3A9I4022740) from the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea to S.Y.L. This work was also supported by the KAIST Cross-Generation Collaborative Laboratory project.
-PublicationSo Young Choi, Jeonghun Oh, JaeHwang Jung, YongKeun Park, and Sang Yup Lee. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individualbacterial cell in its native state. PNAS(https://doi.org./10.1073/pnas.2103956118)
-ProfileDistinguished Professor Sang Yup LeeMetabolic Engineering and Synthetic Biologyhttp://mbel.kaist.ac.kr/
Department of Chemical and Biomolecular Engineering KAIST
Endowed Chair Professor YongKeun ParkBiomedical Optics Laboratoryhttps://bmokaist.wordpress.com/
Department of PhysicsKAIST
2021.07.28 View 10312 -
Observing Individual Atoms in 3D Nanomaterials and Their Surfaces
Atoms are the basic building blocks for all materials. To tailor functional properties, it is essential to accurately determine their atomic structures. KAIST researchers observed the 3D atomic structure of a nanoparticle at the atom level via neural network-assisted atomic electron tomography.
Using a platinum nanoparticle as a model system, a research team led by Professor Yongsoo Yang demonstrated that an atomicity-based deep learning approach can reliably identify the 3D surface atomic structure with a precision of 15 picometers (only about 1/3 of a hydrogen atom’s radius). The atomic displacement, strain, and facet analysis revealed that the surface atomic structure and strain are related to both the shape of the nanoparticle and the particle-substrate interface.
Combined with quantum mechanical calculations such as density functional theory, the ability to precisely identify surface atomic structure will serve as a powerful key for understanding catalytic performance and oxidation effect.
“We solved the problem of determining the 3D surface atomic structure of nanomaterials in a reliable manner. It has been difficult to accurately measure the surface atomic structures due to the ‘missing wedge problem’ in electron tomography, which arises from geometrical limitations, allowing only part of a full tomographic angular range to be measured. We resolved the problem using a deep learning-based approach,” explained Professor Yang.
The missing wedge problem results in elongation and ringing artifacts, negatively affecting the accuracy of the atomic structure determined from the tomogram, especially for identifying the surface structures. The missing wedge problem has been the main roadblock for the precise determination of the 3D surface atomic structures of nanomaterials.
The team used atomic electron tomography (AET), which is basically a very high-resolution CT scan for nanomaterials using transmission electron microscopes. AET allows individual atom level 3D atomic structural determination.
“The main idea behind this deep learning-based approach is atomicity—the fact that all matter is composed of atoms. This means that true atomic resolution electron tomogram should only contain sharp 3D atomic potentials convolved with the electron beam profile,” said Professor Yang.
“A deep neural network can be trained using simulated tomograms that suffer from missing wedges as inputs, and the ground truth 3D atomic volumes as targets. The trained deep learning network effectively augments the imperfect tomograms and removes the artifacts resulting from the missing wedge problem.”
The precision of 3D atomic structure can be enhanced by nearly 70% by applying the deep learning-based augmentation. The accuracy of surface atom identification was also significantly improved.
Structure-property relationships of functional nanomaterials, especially the ones that strongly depend on the surface structures, such as catalytic properties for fuel-cell applications, can now be revealed at one of the most fundamental scales: the atomic scale.
Professor Yang concluded, “We would like to fully map out the 3D atomic structure with higher precision and better elemental specificity. And not being limited to atomic structures, we aim to measure the physical, chemical, and functional properties of nanomaterials at the 3D atomic scale by further advancing electron tomography techniques.”
This research, reported at Nature Communications, was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research M3I3 Project.
-Publication
Juhyeok Lee, Chaehwa Jeong & Yongsoo Yang
“Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography”
Nature Communications
-Profile
Professor Yongsoo Yang
Department of Physics
Multi-Dimensional Atomic Imaging Lab (MDAIL)
http://mdail.kaist.ac.kr
KAIST
2021.05.12 View 9584 -
A Deep-Learned E-Skin Decodes Complex Human Motion
A deep-learning powered single-strained electronic skin sensor can capture human motion from a distance. The single strain sensor placed on the wrist decodes complex five-finger motions in real time with a virtual 3D hand that mirrors the original motions. The deep neural network boosted by rapid situation learning (RSL) ensures stable operation regardless of its position on the surface of the skin.
Conventional approaches require many sensor networks that cover the entire curvilinear surfaces of the target area. Unlike conventional wafer-based fabrication, this laser fabrication provides a new sensing paradigm for motion tracking.
The research team, led by Professor Sungho Jo from the School of Computing, collaborated with Professor Seunghwan Ko from Seoul National University to design this new measuring system that extracts signals corresponding to multiple finger motions by generating cracks in metal nanoparticle films using laser technology. The sensor patch was then attached to a user’s wrist to detect the movement of the fingers.
The concept of this research started from the idea that pinpointing a single area would be more efficient for identifying movements than affixing sensors to every joint and muscle. To make this targeting strategy work, it needs to accurately capture the signals from different areas at the point where they all converge, and then decoupling the information entangled in the converged signals. To maximize users’ usability and mobility, the research team used a single-channeled sensor to generate the signals corresponding to complex hand motions.
The rapid situation learning (RSL) system collects data from arbitrary parts on the wrist and automatically trains the model in a real-time demonstration with a virtual 3D hand that mirrors the original motions. To enhance the sensitivity of the sensor, researchers used laser-induced nanoscale cracking.
This sensory system can track the motion of the entire body with a small sensory network and facilitate the indirect remote measurement of human motions, which is applicable for wearable VR/AR systems.
The research team said they focused on two tasks while developing the sensor. First, they analyzed the sensor signal patterns into a latent space encapsulating temporal sensor behavior and then they mapped the latent vectors to finger motion metric spaces.
Professor Jo said, “Our system is expandable to other body parts. We already confirmed that the sensor is also capable of extracting gait motions from a pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.”
This study was featured in Nature Communications.
Publication:
Kim, K. K., et al. (2020) A deep-learned skin sensor decoding the epicentral human motions. Nature Communications. 11. 2149. https://doi.org/10.1038/s41467-020-16040-y29
Link to download the full-text paper:
https://www.nature.com/articles/s41467-020-16040-y.pdf
Profile: Professor Sungho Jo
shjo@kaist.ac.kr
http://nmail.kaist.ac.kr
Neuro-Machine Augmented Intelligence Lab
School of Computing
College of Engineering
KAIST
2020.06.10 View 10793 -
Professor YongKeun Park Wins the 2018 Fumio Okano Award
(Professor Park)
Professor YongKeun Park from the Department of Physics won the 2018 Fumio Okano Award in recognition of his contributions to 3D display technology development during the annual conference of the International Society for Optics and Photonics (SPIE) held last month in Orlando, Florida in the US.
The Fumio Okano Best 3D Paper Prize is presented annually in memory of Dr. Fumio Okano, a pioneer and innovator of 3D displays who passed away in 2013, for his contributions to the field of 3D TVs and displays. The award is sponsored by NHK-ES.
Professor Park and his team are developing novel technology for measuring and visualizing 3D images by applying random light scattering. He has published numerous papers on 3D holographic camera technology and 3000x enhanced performance of 3D holographic displays in renowned international journals such as Nature Photonics, Nature Communications, and Science Advances. His technology has drawn international attention from renowned media outlets including Newsweek and Forbes.
He has established two startups to commercialize his technology. Tomocube specializes in 3D imaging microscopes using holotomographic technology and the company exports their products to several countries including the US and Japan. The.Wave.Talk is exploring technology for examining pre-existing bacteria anywhere and anytime.
Professor Park’s innovations have already been recognized in and out of KAIST. In February, he was selected as the KAISTian of the Year for his outstanding research, commercialization, and startups. He was also decorated with the National Science Award in April by the Ministry of Science and ICT and the Hong Jin-Ki Innovation Award later in May by the Yumin Cultural Foundation.
Professor Park said, “3D holography is emerging as a significant technology with growing potential and positive impacts on our daily lives. However, the current technology lags far behind the levels displayed in SF movies. We will do our utmost to reach this level with more commercialization."
2018.05.31 View 8960 -
Humicotta Wins the Silver Prize at the 2017 IDEA
The 3D-printed ceramic humidifier made by the research team led by Professor Sang-Min Bae won the silver prize at the 2017 International Design Excellence Awards (IDEA). Professor Bae’s ID+IM team was also listed as winners of three more appropriate technology designs at the IDEA. The awards, sponsored by the Industrial Designers Society of America, are one of the three prestigious design awards including the Red Dot Design Award and the iF Design Award in Germany.
The silver prize winner in the category of home and bath, Humicotta is an energy-efficient, bacteria free, and easy to clean humidifier. It includes a base module and filter. The base is a cylindrical pedestal with a built-in fan on which the filter is placed. The filter is a 3D-printed honeycomb structure made of diatomite. When water is added, the honeycomb structure and porous terracotta maximize natural humidification. It also offers an open platform service that customizes the filters or provides files that users can use their own 3D printer.
Professor Bae’s team has worked on philanthropy design using appropriate technology as their main topic for years. Their designs have been recognized at prestigious global design awards events, winning more than 50 prizes with innovative designs made for addressing various global and social problems.
The Light Funnel is a novel type of lighting device designed for off-grid areas of Africa. It helps to maximize the natural light effect in the daytime without any drastic home renovations. It consists of a transparent acrylic sphere and a reflective pathway. After filling the acrylic sphere with water and placing it on a rooftop, sunlight passes into the house through the water inside the sphere. It provides a lighted environment nine times brighter than without it. Also, once installed, it can be used almost permanently.
The Maasai Smart Cane is made using wood sticks purchased through fair trade with the Maasai tribe. GPS is installed into the grip of the birch-tree cane, so that cane users can send a signal when in an emergency situation. All of the proceeds of this product go to the tribe.
S.Cone is a first aid kit made in collaboration with Samsung Fire and Marine Insurance. The traffic cone-shaped kit is designed to help users handle an emergency situation intact and safe. The S.Cone has unique versions for fires, car accidents, and marine accidents. For example, the S.Cone for fires is equipped with a small fire extinguisher, smoke mask, and fire blanket. The cap of the S.Cone also functions as an IoT station connecting the fire and gas detector with smart phones.
Professor Bae said of his team’s winning design products, “By making the data public, any person can design their own humidifier if they have access to a 3D-printer. We want it to be a very accessible product for the public. The Light Funnel and Maasai Smart Cane are designed for economically-marginalized populations and the elderly. We will continue to make the best designed products serving the marginalized 90% of the population around the world.”
2017.09.14 View 23241 -
Controlling 3D Behavior of Biological Cells Using Laser Holographic Techniques
A research team led by Professor YongKeun Park of the Physics Department at KAIST has developed an optical manipulation technique that can freely control the position, orientation, and shape of microscopic samples having complex shapes. The study has been published online in Nature Communications on May 22.
Conventional optical manipulation techniques called “optical tweezers,” have been used as an invaluable tool for exerting micro-scale force on microscopic particles and manipulating three-dimensional (3-D) positions of particles. Optical tweezers employ a tightly-focused laser whose beam diameter is smaller than one micrometer (1/100 of hair thickness), which can generate attractive force on neighboring microscopic particles moving toward the beam focus. Controlling the positions of the beam focus enabled researchers to hold the particles and move them freely to other locations so they coined the name “optical tweezers,” and have been widely used in various fields of physical and biological studies.
So far, most experiments using optical tweezers have been conducted for trapping spherical particles because physical principles can easily predict optical forces and the responding motion of microspheres. For trapping objects having complicated shapes, however, conventional optical tweezers induce unstable motion of such particles, and controllable orientation of such objects is limited, which hinder controlling the 3-D motion of microscopic objects having complex shapes such as living cells.
The research team has developed a new optical manipulation technique that can trap complex objects of arbitrary shapes. This technique first measures 3-D structures of an object in real time using a 3-D holographic microscope, which shares the same physical principle of X-Ray CT imaging. Based on the measured 3-D shape of the object, the researchers precisely calculates the shape of light that can stably control the object. When the shape of light is the same as the shape of the object, the energy of the object is minimized, which provides the stable trapping of the object having the complicated shape.
Moreover, by controlling the shape of light to have various positions, directions, and shapes of objects, it is possible to freely control the 3-D motion of the object and make the object have a desired shape. This process resembles the generation of a mold for casting a statue having desired shape so the researchers coined the name of the present technique “tomographic mold for optical trapping (TOMOTRAP).” The team succeeded in trapping individual human red blood cells stably, rotating them with desired orientations, folding them in an L-shape, and assembling two red blood cells together to form a new structure. In addition, colon cancer cells having a complex structure could be stably trapped and rotated at desired orientations. All of which have been difficult to be realized by the conventional optical techniques.
Professor Park said, “Our technique has the advantage of controlling the 3-D motion of complex shaped objects without knowing prior information about their shape and optical characteristics, and can be applied in various fields including physics, optics, nanotechnology, and medical science.”
Dr. Kyoohyun Kim, the lead author of this paper, noted that this technique can induce controlled deformation of biological cells with desired shapes. “This approach can be also applied to real-time monitoring of surgical prognosis of cellular-level surgeries for capturing and deforming cells as well as subcellular organelles,” added Kim.
Figure 1. Concept of optical manipulation techniques
Figure 2. Experimental setup
Figure 3. Research results
2017.05.25 View 7866 -
Professor Jinah Park Received the Prime Minister's Award
Professor Jinah Park of the School of Computing received the Prime Minister’s Citation Ribbon on April 21 at a ceremony celebrating the Day of Science and ICT. The awardee was selected by the Ministry of Science, ICT and Future Planning and Korea Communications Commission.
Professor Park was recognized for her convergence R&D of a VR simulator for dental treatment with haptic feedback, in addition to her research on understanding 3D interaction behavior in VR environments. Her major academic contributions are in the field of medical imaging, where she developed a computational technique to analyze cardiac motion from tagging data.
Professor Park said she was very pleased to see her twenty-plus years of research on ways to converge computing into medical areas finally bear fruit. She also thanked her colleagues and students in her Computer Graphics and CGV Research Lab for working together to make this achievement possible.
2017.04.26 View 7935