In this paper, we develop a fully-buried transfer gate and polysilicon wire (FBP) structure that significantly improves noise performance in a 2-layer pixel CMOS image sensor. FBP converts all the devices made of poly-Si into fully buried structures and interconnects multiple floating diffusion nodes using buried poly-Si wire, thereby simultaneously reducing process steps and improving random noise. The Conversion Gain (CG) was improved by more than 33% compared to the conventional scheme.

In this article, a world's smallest 0.7 mu m-pitch dual photodiode pixel is presented. We integrated 2-layer pixel with hybrid Cu-Cu bonding process only, without introducing pixel-level deep contacts. By optimizing layout of Cu pad layer, we suppressed capacitive coupling between neighboring floating diffusion nodes, still achieved similar conversion gain compared to that of 0.7 mu m-pitch, 1-layer single photodiode pixel. We overcome the degradation of the auto-focus (AF) separation ratio by incorporating multi-focal, metaphotonic color routers (MPCR).

64Mp CIS with 0.5um pixels has been developed with three wafer layers (e.g. top-wafer for PDs and TG TRs, mid-wafer for pixel TRs, and bottom-wafer for the analog and logic circuits). The RTS noise was reduced by 85% compared to ones of the conventional structure with over 6,000e-FWC as similar to our previous research [1] - [3]. In addition, the FD conversion gain was improved by 67% with the Miller effect due to the reduction of the DCNT capacitance.

CMOS image sensors (CISs) with deep-submicron pixels are now in high demand, as high-end mobile devices are equipped with multiple camera modules that are used for ultra-high-resolution imaging [1], [2]. The biggest challenges with small pixels are to maintain dynamic range (DR), signal-to-noise ratio (SNR), and sensitivity compatible with a sensor with larger pixels. A back-illuminated stacked sensor with front deep-trench isolation (FDTI) and shallow-trench isolation (STI) for inter-pixel and inter-node isolation respectively appears promising for continuous pixel-size reduction in terms of maximizing DR with large full-well capacity (FWC) while minimizing optical/electrical crosstalk [3]–[5]. However, dark current may increase by strong electric fields (e-field) near defective FDTI interfaces if more doping is applied to a small photodiode (PD) to increase FWC. In addition, the FDTI/STI structure limits the area of in-pixel transistor amplifiers, and it may deteriorate dark temporal noise (TN) as pixel pitch becomes smaller. Furthermore, optical crosstalk between different color filters (CFs) is more problematic as pixel size enters the sub-wavelength scale. In this work, a back-illuminated 64Mpixel CIS with $0.56\mu\mathrm{m}$ -pitched pixels is reported. We present pixel designs and fabrication processes that achieve competitive FWC, dark current, TN, and optical performances with small pixels.

For years, there has been a strong drive for sub-micron pixel development, in spite of reaching the visible light diffraction limit, because a smaller pixel pitch of CMOS image sensors (CISs) is inevitably required for ever-miniaturizing camera modules as mobile devices incorporate more cameras, few of which are dedicated to ultra-high-resolution zoomed images [1]. To that end, image sensor vendors have tried to find new ways to avoid reduction in sensitivity and more crosstalk in the sensor through pixel architecture change and/or fabrication process refinement [2-4]. For example, a 0.7μm pixel sensor was demonstrated with acceptable photodiode (PD) full-well capacity (FWC) of >6,000eas well as signal-to-noise ratio (SNR) of -32dB without optical/electrical crosstalk by employing state-of-the-art full-depth deep-trench isolations (FDTIs). [4] However, further scaling requires elaborate fabrication innovation and layout ideas. At the same time, meeting every aspect of pixel performance compared to the previous generation becomes even more difficult, e.g., with respect to dark or illuminated characteristics, fixed-pattern or temporal noises, etc. The latter, in particular, is associated with in-pixel source-follower (SF) amplifiers. Therefore, electrical performance of scaled in-pixel transistors cannot be overlooked. In this paper, a 32-megpixel (MP) CIS with 0.64μm unit pixels is demonstrated with FDTI design. Innovations in terms of fabrication and design to achieve this performance with scaling are discussed.

As the pixel size is scaling down due to the market demand particularly of the mobile CMOS image sensor (CIS) market, the distance between a transfer gate (TG) transistor and a floating diffusion node (FD) is becoming smaller. Consequently, the leakage current at FD nodes by gate-induced drain leakage (GIDL) is a primary source of image defects such as multi-bit white spots particularly where FD nodes are shared for adaptive pixel-level gain control as well as sensitivity improvement at low illumination. In this work, vertically-etched TGs (VTGs) were integrated in a 0.64μm-pixel sensor for better charge transfer from photodiodes as well as smaller pixel area. We found that GIDL of VTGs mainly arises from trap-assisted tunneling (TAT) at the gate controlled FD junction diode with thermal activation. The leakage current exponentially increases with electric field at the drain node of VTGs, which was correlated with overlap capacitance (Cov) between VTG and FD. We were able to mitigate multi-bit white spot defects by optimizing the dry etch condition of VTGs and doping profiles of FD in order to minimize the chip-level variation of Cov. Keywords—CMOS Image Sensor, Transfer Gate Transistor, Gate-induced Drain Leakage, Trap-assisted tunneling.

As the smart mobile device market continues to grow and the number of cameras per device rapidly increases, demand for CMOS image sensors (CIS) also increases. Two major trends in mobile device cameras are: (1) adopting smaller pixels that enable greater pixel count at similar optical format, and (2) bigger pixels for higher image quality. To be more specific, front-facing cameras have a trend towards smaller pixels, while rear main cameras have a trend towards both smaller and bigger pixels. The optical format of front-facing cameras is especially limited due to existing bezel-less or border-less display designs, yet higher resolution still-shot and video (such as 4K UHD) recording is desired. To implement greater pixel count in a limited camera module size, scaling of pixel size is required. The main challenges are to maintain acceptable photodiode full-well capacity (FWC) and sensitivity, while suppressing optical crosstalk [1]. To completely eliminate both electrical and optical crosstalk, deep-trench isolation (DTI) has evolved from early BDTI (Back-side DTI) to current FDTI (Front-side DTI) technology, which is also called full-depth DTI. In this paper, a 44Mpixel CIS with 0.7μm pixels using full-depth DTI is demonstrated.

LRF를 이용한 SLAM 기반 AGV는 초기 가동 시 전역지도를 이용하여 초기위치를 추정해야한다. 하지만, LRF 센서를 통해서 얻을 수 있는 환경정보가 적기 때문에, AGV의 초기위치를 찾기 어렵다는 문제가 있다. 본 논문은 이러한 초기위치추정 문제를 해결하기 위해 스테레오 카메라를 통해 얻은 이미지의 특징 정보 및 3D 좌표 정보의 기하학적 특성을 이용하여 초기위치를 추정하는 방식을 이용하였다. 보다 정확한 위치 추정을 위해, 위치 추정 단계 중 특징 매칭을 하는 수행하는 단계에서, 가버 웨이블릿을 이용하여 오매칭을 필터링함으로써 초기위치추정 정확도를 향상시키는 방법을 제안한다. 실제 계측 데이터를 이용하여 초기위치추정 실험을 수행한 결과, 오매칭 필터링 과정을 수행하지 않았을 때보다 위치 추정 정확도가 향상됨을 확인하였다.

Autonomous driving system has become a hot issue. Similar to autonomous driving system, Autonomous Guided Vehicle (AGV) works in industry field. It is controlled by its autonomous driving system. This system detects the driving environment by using sensors such as vision sensor, laser sensor, ultrasonic sensor and so on. Among these sensors, vision sensor can obtain various kinds of the information of the environment. In an image, which is captured by vision sensor, the environment is represented by its color, shape and so on. Vision-AGV where vision sensor is mounted can be controlled by the colorful sign like the traffic light. To recognize the sign for controlling the AGV, it is necessary to detect the sign and analyze the meaning of the this sign. Faster Regions with Convolutional Neural Network features (Faster R-CNN) is applied to detect the sign. To analyze the meaning of the color sign, RGB color space, which is well known and consists of primary colors, can be used. However, it is difficult to analyze the color sign by using only RGB color space. Therefore, the proposed method is designed for detecting and recognizing the color of the sign with CIE L*a*b* color space. To improve the effect of the analysis, RGB and CIE L*a*b* color space are combined. Based on RGB, CIE L*a*b* and Peak Signal-toNoise Ratio (PSNR), the color of the region which is extracted by Faster R-CNN is determined by the fuzzy inference system. As experimental results, the proposed method can detect the sign and analyze the color of this sign.

A lot of researches have been made on how to know the position of the mobile robot when it knows the initial position of the mobile robot. For the robot to be completely unmanned, the robot also needs to find out its own initial position. To do this, it is necessary to obtain enough data to estimate the initial position without previous data. In Simultaneous Localization and Mapping (SLAM), Light Detection and Ranging (LiDAR) is often used to obtain accurate map for mobile robot. In this case, it is difficult to find the initial position because there is little information at initial start-up. On the other hand, stereo camera has the advantage that it can acquire more information than LiDAR by acquiring spatial information with the same principle as the human eye. However, the obtained 3D spatial information has a disadvantage of low precision. The purpose of this paper is to form a 3D map to be used for finding the initial position by linking LRF information to compensate for the low accuracy of the 3D map made only by the stereo camera in the environment.

This paper proposes the method to adjust brightness information by applying CIE L∗a∗b∗ color space and adaptive neuro-fuzzy inference system. The image which is already captured by vision sensor should be adjusted brightness to recognize objects in an image. In case of proper intensity of lights, the clarity of an image is good to recognize objects. However, in case of improper intensity of lights, the image has darkish regions. It will leads to reduce success of object recognition. To make up for this week point, we adjust the image, which is a darkish image, by controlling brightness information of an image. Brightness information can be represented by CIE L∗a∗b∗ color space. So based on CIE L∗a∗b∗ color space, adaptive neuro-fuzzy inference system is implemented as control function. Control function carries out adjusting of brightness information by dealing with the value of L component of CIE L∗a∗b∗ color space. L component describes brightness information of an image. The values which is calculated by adaptive neuro-fuzzy inference system is called the adjustment coefficient. Finally, the adjustment coefficient is added to L component for adjusting brightness information. To verify the propose method, we calculated color difference with respect to RGB and CIE L∗a∗b∗ color space. As experimental results, the propose method can reduce color difference and makes the target image will be similar with reference image under proper intensity of lights.

A wide range of per- and polyfluoroalkyl substances (PFASs), including fluorotelomer alcohols (FTOHs), perfluorooctane sulfonamidoethanols (FOSEs), perfluoroalkyl carboxylic acids (PFCAs), and perfluoroalkane sulfonic acids (PFSAs), were measured in fifteen house dust and two nonresidential indoor dust of Korea. Total concentrations of PFASs in house dust ranged from 29.9 to 97.6ngg−1, with a dominance of perfluorooctane sulfonic acid (PFOS), followed by 8:2 FTOH, N-Ethyl perfluorooctane sulfonamidoethanol (EtFOSE), perfluoroctanoic acid (PFOA). In a typical exposure scenario, the estimated daily intakes (EDIs) of total PFASs via house dust ingestion were 2.83ngd−1 for toddlers and 1.13ngd−1 for adults, which were within the range of the mean EDIs reported from several countries. For PFOA and PFOS exposure via house dust ingestion, indirect exposure (via precursors) was a minor contributor, accounting for 5% and 12%, respectively. An aggregated exposure (hereafter, overall-EDIs) of PFOA and PFOS occurring via all pathways, estimated using data compiled from the literature, were 53.6 and 14.8ngd−1 for toddlers, and 20.5 and 40.6ngd−1 for adults, respectively, in a typical scenario. These overall-EDIs corresponded to 82% (PFOA) and 92% (PFOS) of a pharmacokinetic model-based EDIs estimated from adults' serum data. Direct dietary exposure was a major contributor (>89% of overall-EDI) to PFOS in both toddlers and adults, and PFOA in toddlers. As for PFOA exposure of adults, however direct exposure via tap water drinking (37%) and indirect exposure via inhalation (22%) were as important as direct dietary exposure (41%). House dust-ingested exposure (direct+indirect) was responsible for 5% (PFOS in toddlers) and <1% (PFOS in adults, and PFOA in both toddlers and adults) of the overall-EDIs. In conclusion, house-dust ingestion was a minor contributor in this study, but should not be ignored for toddlers' PFOS exposure due to its significance in the worst-case scenario.

This study focused on a quantitative substance flow analysis (SFA) of polybrominated diphenyl ethers (PBDEs) in plastics from obsolete TVs and computer monitors that often contain large amounts of the flame retardants. According to the results of the static SFA study, 1.87 tons and 0.28 tons of PBDEs from newly manufactured TVs and computer monitors were introduced into households in 2011 in Korea, respectively. There were approximately 924 tons and 90.3 tons of PBDEs present in TVs and computer monitors in households during product use, respectively. The results of the dynamic SFA study indicated that in 2017 the amount of PBDEs from TVs and computer monitors in the recycling stage is expected to be 2.63 tons and 0.1 tons, respectively. Large fractions of PBDEs from used TVs are present in recycled plastics, while PBDE-containing computer monitors are exported to Southeast Asian countries. This research indicates that PBDEs were emitted the most from recycled plastic pellet processes upon recycling. Further study may be warranted to focus the flow of PBDEs in recycled plastic products in order to determine the final destination and disposal of these chemicals in the environment.

The digital substations are being built based on the IEC 61850 network. The cooperation and protection of power system are becoming more intelligent and reliable in the environment of digital substation. This paper proposes a novel method to prevent the malfunction caused by the Transformer Magnetizing Inrush Current(TMIC) using the IEC 61850 based data sharing between the IEDs. To protect a main transformer, the current differential protection(87T) and over-current protection(50/51) are used generally. The 87T IED applies to the second harmonic blocking method to prevent the malfunction caused by the TMIC. However, the 50/51 IED may malfunction caused by the TMIC. To solve that problem, the proposed method uses a GOOSE inter-lock signal between two IEDs. The 87T IED transmits a blocking GOOSE signal to the 50/51 IED, when the TMIC is detected. The proposed method can make a cooperation of digital substation protection system more intelligent. To verify the performance of proposed method, this paper performs the real time test using the RTDS (Real Time Digital Simulator) test-bed. Using the RTDS, the power system transients are simulated, and the TMIC is generated. The performance of proposed method is verified in real-time using that actual current signals. The reaction of simulated power system responding to the operation of IEDs can be also confirmed.

In this study, we monitored the newly added Stockholm Convention persistent organic pollutants (POPs) HCHs, PeCBz, endosulfans, chlordecone, PBDEs, PBBs and PFCs in industrial, urban, and agricultural soils in South Korea, in order to evaluate their distributions and potential sources. These POPs were widely distributed throughout South Korea, and their concentrations and distributions were affected by land use, reflecting their sources. The overall concentrations of HCHs, PeCBz, endosulfans, PBDEs, and PFCs in soils were in the range of ND (non-detectable)–0.358 ng/g (average ± standard deviation: 0.060 ± 0.080 ng/g), ND–0.531 ng/g (0.083 ± 0.133 ng/g), 0.058–8.42 ng/g (2.19 ± 2.43 ng/g), 0.004–4.78 ng/g (0.68 ± 1.06 ng/g), and ND–1.62 ng/g (0.50 ± 0.46 ng/g), respectively. Agricultural soils showed the highest concentration of endosulfan, which was the most recently used pesticide monitored in this study. On the other hand, industrial soils contained the highest concentrations of PeCBz, PBDEs, and PFCs, which were mainly introduced to environment via the industrial activities.

In this paper, the travel control of the spherical wheeled robot with a mecanum wheel is impelemented. Four typical wheels or three omni wheels are used to consist of the ball-bot. the slip is occured when the typical wheels is used to the ball-bot. In order to reduce these slip, the spherical wheeled robot with macanum wheels is proposed. Through some experiments, we find that the proposed spherical wheeled robot with a mecanum wheel is superior to the conventional spherical wheeled robot with typical wheels.