• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.591-598
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• 2016

The final step of driving a car is parking, which is the most difficult part for people learning to drive. Parking in narrow parking spaces is difficult for both ordinary drivers and beginners. To solve this problem, the development of SPAS (Smart Parking Assist System), ACC (Automatic Control System) improves the convenience of drivers. In addition, parking assistance systems have been developed to recognize more accurately the surrounding environment to the driver using the ultrasound, camera, thermal camera, and radar. This paper proposes the reverse turning radius to process images as if the camera is located in the center of the vehicle regardless of the actual camera position. In addition, it generates the parking guidelines through verification using the vehicle.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.595-604
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• 2016

Utilizing a standard 130-nm CMOS process, a RF frontend is designed at 24 GHz for automotive collision avoidance radar application. Single IF direct conversion receiver (DCR) architecture is adopted to achieve high integration level and to alleviate the DCR problem. The proposed frontend is composed of a two-stage LNA and downconversion mixers. To save power consumption, and to enhance gain and linearity, stacked NMOS-PMOS $g_m$-boosting technique is employed in the design of LNA as the first stage. The switch transistors in the mixing stage are biased in subthreshold region to achieve low power consumption. The single balanced mixer is designed in PMOS transistors and is also realized based on the well-known folded architecture to increase voltage headroom. This frontend circuit features enhancement in gain, linearity, and power dissipation. The proposed circuit showed a maximum conversion gain of 19.6 dB and noise figure of 3 dB at the operation frequency. It also showed input and output return losses of less than -10 dB within bandwidth. Furthermore, the port-to-port isolation illustrated excellent characteristic between two ports. This frontend showed the third-order input intercept point (IIP3) of 3 dBm for the whole circuit with power dissipation of 6.5 mW from a 1.5 V supply.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.3
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• pp.274-283
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• 2014

In this paper, the epi layer of npn SOI HBT with n+ buried layer has been studied through Sentaurus process and device simulator. The doping value of the deposited epi layer has been varied for the npn HBT to achieve improved $f_tBV_{CEO}$ product (397 GHzV). As the $BV_{CEO}$ value is higher for low value of epi layer doping, higher supply voltage can be used to increase the $f_t$ value of the HBT. At 1.8 V $V_{CE}$, the $f_tBV_{CEO}$ product of HBT is 465.5 GHzV. Further, the film thickness of the epi layer of the SOI HBT has been scaled for better performance (426.8 GHzV $f_tBV_{CEO}$ product at 1.2 V $V_{CE}$). The addition of this HBT module to fully depleted SOI CMOS technology would provide better solution for realizing wireless circuits and systems for 60 GHz short range communication and 77 GHz automotive radar applications. This SOI HBT together with SOI CMOS has potential for future high performance SOI BiCMOS technology.

• JSTS:Journal of Semiconductor Technology and Science
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• v.8 no.4
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• pp.289-294
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• 2008

A 77 GHz 3-stage low noise amplifier (LNA) employing one common source and two cascode stages is developed using $0.13{\mu}m$ CMOS process. To compensate for the low gain which is caused by lossy silicon substrate and parasitic element of CMOS transistor, positive feedback technique using parasitic inductance of bypass capacitor is adopted to cascode stages. The developed LNA shows gain of 7.2 dB, Sl1 of -16.5 dB and S22 of -19.8 dB at 77 GHz. The return loss bandwidth of LNA is 71.6 to 80.9 GHz (12%). The die size is as small as $0.7mm\times0.8mm$ by using bias line as inter-stage matching networks. This LNA shows possibility of 77 GHz automotive RADAR system using $0.13{\mu}m$ CMOS process, which has advantage in cost compared to sub-100 nm CMOS process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.760-761
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• 2013

본 논문은 차량 추돌 예방 레이더용 24GHz 전압제어발진기를 제안한다. 이러한 회로는 TSMC $0.13{\mu}m$ 혼성신호/고주파 CMOS 공정($f_T/f_{MAX}=120/140GHz$)으로 설계되어 있다. 이러한 회로는 스위치형 공진기 (switched resonator)의 기본 구조를 지닌 24GHz 주파수 대역을 사용할 수 있도록 CMOS LC 튜닝 회로를 포함하고 있다. 특히 전체 칩 면적을 줄이기 위해 수동형 인덕터 대신 능동형 인덕터부를 사용하였다. 본 연구에서 개발한 발진기는 전체 튜닝 범위에 대해 24GHz에서 8%의 측정결과를 보였으며, 600kHz 오프셋에서 24GHz에 대해 약 -89dBc/Hz의 우수한 위상 잡음 특성을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.702-703
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• 2013

본 논문은 차량 추돌 예방 레이더용 24GHz 전압제어발진기를 제안한다. 이러한 회로는 TSMC $0.13{\mu}m$ 혼성신호/고주파 CMOS 공정($f_T/f_{MAX}=120/140GHz$)으로 설계되어 있다. 이러한 회로는 스위치형 공진기 (switched resonator)의 기본 구조를 지닌 24GHz 주파수 대역을 사용할 수 있도록 CMOS LC 튜닝 회로를 포함하고 있다. 특히 전체 칩 면적을 줄이기 위해 수동형 인덕터 대신 능동형 인덕터부를 사용하였다. 본 연구에서 개발한 발진기는 전체 튜닝 범위에 대해 24GHz에서 8%의 측정 결과를 보였으며, 600kHz 오프셋에서 24GHz에 대해 약 -89dBc/Hz의 우수한 위상 잡음 특성을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.873-874
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• 2015

본 논문에서는 24~77GHz 대역의 충돌 방지 레이더 센서를 이용한 차량 충돌 방지 알고리즘을 제안한다. 제안한 알고리즘은 센서로 부터 측정한 전압을 이용하여 전/후/좌/우의 차량의 접근 정보를 획득하고 이를 효율적으로 이용하여 다양한 상황에 따른 차량충돌방지를 할 수 있도록 구현되어 있다. 제안한 차량 충돌방지 알고리즘은 운행 중인 속도를 기반으로 속도구간별 운행정보를 계산하여 충돌방지를 실행한다. 본 연구에서 구현한 차량 충돌 방지 알고리즘은 차량 주행에서 좌우 차량충돌 없이 효율적으로 운행하는 특성을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.765-767
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• 2014

본 논문에서는 단거리 레이더용 차량 추돌 방지 24-GHz CMOS 고주파 전력 증폭기 (RF Power Amplifier)를 제안한다. 이러한 회로는 class-A 모드 증폭기로서 단간 (inter-stages) 공액 정합 (conjugate matching) 회로를 가진 공통-소스 단으로 구성되어 있다. 칩 면적을 줄이기 위해 실제 인덕터 대신 전송선(Transmission Line)을 이용하였다. 제안한 회로는 TSMC $0.13{\mu}m$ 혼성 신호/고주파 CMOS 공정 ($f_T/f_{MAX}=120/140GHz$)으로 설계하였다. 설계한 CMOS 고주파 전력 증폭기는 최근 발표된 연구결과에 비해 약 22dB의 높은 전력이득 및 7.1%의 높은 PAE 특성을 보였다.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.