• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.9
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• pp.729-734
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• 2000

Recently, Very Large Scale Integrated (VLSI) circuit & deep-submicron bulk Complementary Metal Oxide Semiconductor(CMOS) devices require gate electrode materials such as metal-silicide, Titanium-silicide for gate oxides. Many previous authors have researched the improvement sub-micron gate oxide quality. However, few have reported on the electrical quality and reliability on the ultra thin gate oxide. In this paper, at first, I recommand a novel shallow trench isolation structure to suppress the corner metal-oxide semiconductor field-effect transistor(MOSFET) inherent to shallow trench isolation for sub 0.1${\mu}{\textrm}{m}$ gate oxide. Different from using normal LOCOS technology deep-submicron CMOS devices using novel Shallow Trench Isolation(STI) technology have a unique"inverse narrow-channel effects"-when the channel width of the devices is scaled down, their threshold voltage is shrunk instead of increased as for the contribution of the channel edge current to the total channel current as the channel width is reduced. Secondly, Titanium silicide process clarified that fluorine contamination caused by the gate sidewall etching inhibits the silicidation reaction and accelerates agglomeration. To overcome these problems, a novel Two-step Deposited silicide(TDS) process has been developed. The key point of this process is the deposition and subsequent removal of titanium before silicidation. Based on the research, It is found that novel STI structure by the SEM, in addition to thermally stable silicide process was achieved. We also obtained the decrease threshold voltage value of the channel edge. resulting in the better improvement of the narrow channel effect. low sheet resistance and stress, and high threshold voltage. Besides, sheet resistance and stress value, rms(root mean square) by AFM were observed. On the electrical characteristics, low leakage current and trap density at the Si/SiO$_2$were confirmed by the high threshold voltage sub 0.1${\mu}{\textrm}{m}$ gate oxide.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.229-234
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• 1999

We analyzed the reliability characteristics in platinum schottky diodes with variations of n-type silicon substrates concentrations and temperature variations of measurements. The parameters of reliability measurement analysis are saturation current. turn-on voltage and ideality factor in the forward bias, the breakdown voltage in the reverse bias with device shapes. The shape of devices are square type and long rectangular type for edge effect. As a result, we analyzed that the forward turn-on voltage, barrier height, dynamic resistance and reverse breakdown voltage were decreased but ideality factor and saturation current were increased by increased concentration in platinum and n-silicon junction parts. In measurement temperature(RT, $50^{\circ}C$, $75^{\circ}C$), the extracted electrical parameter values of reliability characteristics were increased at the higher temperature under the forward and reverse bias. The long rectangular type devices were more decreased than the square type in reverse breakdown voltage by tunneling effects of edge part.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• Journal of IKEEE
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• v.19 no.4
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• pp.491-499
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• 2015

Silicon Carbide(SiC) has large advantage in high temperature and high voltage applications because of its high thermal conductivity and large band gap energy. When using SiC to design power semiconductor devices, edge termination techniques have to be adjusted for its maximum breakdown voltage characteristics. Many edge termination techniques have been proposed, and the most appropriate technique for SiC device is Junction Termination Extension(JTE). In this paper, the change of breakdown voltage efficiency ratio according to the change of doping concentration and passivation oxide charge of each JTE techniques is demonstrated. As a result, the maximum breakdown voltage ratio of Single Zone JTE(SZ-JTE), Double Zone JTE(DZ-JTE), Multiple Floating Zone JTE(MFZ-JTE), and Space Modulated JTE(SM-JTE) is 98.24%, 99.02%, 98.98%, 99.22% each. MFZ-JTE has the smallest and SZ-JTE has the largest sensitivity of breakdown voltage ratios according to the change of JTE doping concentration. Additionally the degradation of breakdown voltage due to the passivation oxide charge is analyzed, and the sensitivity is largest in SZ-JTE and smallest in MFZ-JTE, too. In this paper, DZ-JTE and SM-JTE is the best efficiency JTE techniques than MFZ-JTE which needs large doping concentration in short JTE width.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.470-476
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• 2020

In this paper, we present the design of modular replaceable AI server for image deep learning that separates the server from the Edge Device so as to drive the AI block and the method of data transmission and reception. The modular replaceable AI server for image deep learning can reduce the dependency between social robots and edge devices where the robot's platform will be operated to improve drive stability. When a user requests a function from an AI server for interaction with a social robot, modular functions can be used to return only the results. Modular functions in AI servers can be easily maintained and changed by each module by the server manager. Compared to existing server systems, modular replaceable AI servers produce more efficient performance in terms of server maintenance and scale differences in the programs performed. Through this, more diverse image deep learning can be included in robot scenarios that allow human-robot interaction, and more efficient performance can be achieved when applied to AI servers for image deep learning in addition to robot platforms.

• Applied Microscopy
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• v.45 no.3
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• pp.107-114
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• 2015

Two-dimensional (2D) materials containing hole defects are a promising substitute for conventional nanopore membranes like silicon nitride. Hole defects on 2D materials, as atomically thin nanopores, have been used in nanopore devices, such as DNA sensor, gas sensor and purifier at lab-scale. For practical applications of 2D materials to nanopore devices, researches on characteristics of hole defects on graphene, hexagonal boron nitride and molybdenum disulfide have been conducted precisely using transmission electron microscope. Here, we summarized formation, features, structural preference and stability of hole defects on 2D materials with atomic-resolution transmission electron microscope images and theoretical calculations, emphasizing the future challenges in controlling the edge structures and stabilization of hole defects. Exploring the properties at the local structure of hole defects through in situ experiments is also the important issue for the fabrication of realistic 2D nanopore devices.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.421-424
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• 2004

As the deep sub-micron devices are recently integrated high package density, novel process method for sub $0.1{\mu}m$ devices is required to get the superior thin gate oxide characteristics and reliability. However, few have reported on the electrical quality and reliability on the thin gate oxide. In this paper I will recommand a novel shallow trench isolation structure for thin gate oxide $30{\AA}$ of deep sub-micron devices. Different from using normal LOCOS technology, novel shallow trench isolation have a unique 'inverse narrow channel effects' when the channel width of the devices is scaled down shallow trench isolation has less encroachment into the active device area. Based on the research, I could confirm the successful fabrication of shallow trench isolation(STI) structure by the SEM, in addition to thermally stable silicide process was achiever. I also obtained the decrease threshold voltage value of the channel edge and the contact resistance of $13.2[\Omega/cont.]$ at $0.3{\times}0.3{\mu}m^2$. The reliability was measured from dielectric breakdown time, shallow trench isolation structure had tile stable value of $25[%]{\sim}90[%]$ more than 55[sec].

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.135-140
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• 2014

As the digital image devices are widely used, interests in the software- and the hardware-related image processing become higher and the image processing techniques are applied in various fields such as object recognition, object detection, fingerprint recognition, and etc. For the edge detections Sobel, Prewitt, Laplacian, Roberts and Canny detectors are used and these existing methods can excellently detect the edges of the images without noise. However, in the images corrupted by the impulse noise, these methods are insufficent in noise elimination characteristics, showing unsatisfactory edge detection. Therefore in this paper, in order to obtain excellent edge detection characteristics in the corrupted image by the impulse noise, an detection algorithm is porposed, which uses the central pixel of mask divided by four regions along the axis, calculates the estimated mask according to the representing pixel values in each regions, and detects the final edges by applying the estimates mask and the new directional one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.69-85
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• 2019

BeiDou navigation satellite system (BDS) is one of the four main types of global navigation satellite systems. The current system has been widely used by the military and by the aerospace, transportation, and marine fields, among others. However, challenges still remain in the BeiDou system, which requires rapid responses for delay-sensitive devices. A differential positioning algorithm called the data center-based differential positioning (DCDP) method is widely used to avoid the influence of errors. In this method, the positioning information of multiple base stations is uploaded to the data center, and the positioning errors are calculated uniformly by the data center based on the minimum variance or a weighted average algorithm. However, the DCDP method has high delay and overload risk. To solve these problems, this paper introduces edge computing to relieve pressure on the data center. Instead of transmitting the positioning information to the data center, a novel method called edge computing-based differential positioning (ECDP) chooses the nearest reference station to perform edge computing and transmits the difference value to the mobile receiver directly. Simulation results and experiments demonstrate that the performance of the ECDP outperforms that of the DCDP method. The delay of the ECDP method is about 500ms less than that of the DCDP method. Moreover, in the range of allowable burst error, the median of the positioning accuracy of the ECDP method is 0.7923m while that of the DCDP method is 0.8028m.