• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.32-32
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• 2011

Plasma etching is used in microelectronic processing for patterning of micro- and nano-scale devices. Commonly, optical emission spectroscopy (OES) is widely used for real-time endpoint detection for plasma etching. However, if the viewport for optical-emission monitoring becomes blurred by polymer film due to prolonged use of the etching system, optical-emission monitoring becomes impossible. In addition, when the exposed area ratio on the wafer is small, changes in the optical emission are so slight that it is almost impossible to detect the endpoint of etching. For this reason, as a simple method of detecting variations in plasma without contamination of the reaction chamber at low cost, a method of measuring plasma impedance is being examined. The object in this research is to investigate the suitability of using plasma impedance monitoring (PIM) with statistical approach for real-time endpoint detection of $SiO_2$ etching. The endpoint was determined by impedance signal variation from I-V monitor (VI probe). However, the signal variation at the endpoint is too weak to determine endpoint when $SiO_2$ film on Si wafer is etched by fluorocarbon plasma on inductive coupled plasma (ICP) etcher. Therefore, modified principal component analysis (mPCA) is applied to them for increasing sensitivity. For verifying this method, detected endpoint from impedance analysis is compared with optical emission spectroscopy (OES). From impedance data, we tried to analyze physical properties of plasma, and real-time endpoint detection can be achieved.

• Journal of Information Technology and Architecture
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• v.11 no.2
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• pp.143-155
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• 2014

In recent years, domestic electronics, banking, electricity, services, manufacturing, pharmaceutical, corporate type and malicious hackers is corporate security through the accident occurred and the resulting loss of corporate information and the damage each year is steadily increasing. Many companies have responded to domestic business activities and to protect critical information related to laptops, smart phones, tablets, and introduced a variety of Endpoint security solutions within. However, being introduced to senselessly Endpoint security solution across the over-budget, with the same features and performance, such as conflicts and problems arise, resulting in additional maintenance costs, in an effort to resolve the conflict in the operational security of the IT department's new difficulty in becoming. Here is the introduction and operation of these Endpoint security solutions in order to solve the problem on employees's PC into the center of the information security governance based on Endpoint security solution to provide the process for determining the solutions presented.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.153-153
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• 2012

Endpoint detection with plasma impedance monitoring and self-plasma optical emission spectroscopy is demonstrated for dielectric layers etching processes. For in-situ detecting endpoint, optical-emission spectroscopy (OES) is used for in-situ endpoint detection for plasma etching. However, the sensitivity of OES is decreased if polymer is deposited on viewport or the proportion of exposed area on the wafer is too small. To overcome these problems, the endpoint was determined by impedance signal variation from I-V monitoring (VI probe) and self-plasma optical emission spectroscopy. In addition, modified principal component analysis was applied to enhance sensitivity for small area etching. As a result, the sensitivity of this method is increased about twice better than that of OES. From plasma impedance monitoring and self-plasma optical emission spectroscopy, properties of plasma and chamber are analyzed, and real-time endpoint detection is achieved.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.182-182
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• 2012

Plasma etching is used in various semiconductor processing steps. In plasma etcher, optical- emission spectroscopy (OES) is widely used for in-situ endpoint detection. However, the sensitivity of OES is decreased if polymer is deposited on viewport or the proportion of exposed area on the wafer is too small. Because of these problems, the object is to investigate the suitability of using plasma impedance monitoring (PIM) and self plasma optical emission spectrocopy (SPOES) with statistical approach for in-situ endpoint detection. The endpoint was determined by impedance signal variation from I-V monitor (VI probe) and optical emission signal from SPOES. However, the signal variation at the endpoint is too weak to determine endpoint when $SiO_2$ and SiNx layers are etched by fluorocarbon on inductive coupled plasma (ICP) etcher, if the proportion of $SiO_2$ and SiNx area on Si wafer are small. Therefore, modified principal component analysis (mPCA) is applied to them for increasing sensitivity. For verifying this method, detected endpoint from impedance monitoring is compared with optical emission spectroscopy.

• Convergence Security Journal
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• v.18 no.3
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• pp.19-25
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• 2018

Endpoint security is a method of ensuring network security by thoroughly protecting multiple individual devices connected to the network. In this study, we survey the functions and features of various commercial products of endpoint security. Also we emphasizes the importance of endpoint security to respond to the increasingly intelligent and sophisticated security threats against the cloud, mobile, artificial intelligence, and IoT based sur-connection era. and as a way to improve endpoint security, we suggest the ways to improve the life cycle of information security such as preemptive security policy implementation, real-time detection and filtering, detection and modification.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.9
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• pp.701-709
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• 2011

In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model. The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2001.05a
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• pp.118-118
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• 2001

Lemna gibba에 대한 성장저해시험은 OECD Lemna Growth Inhibition Test(1998)와 EPA Auqatic Toxicity Test Using Lemna sp, (OPPTS 850.4400)에 제시된 시험 방법으로, 시험물질에 대한 수중식물의 독성값을 알기 위하여 광범위하게 사용되고 있다. 본 실험은 각각의 Guideline에서 제시된 endpoint를 서로 비교하여, 가장 효과적으로 시험의 결과를 표현 할 수 있는 endpoint를 찾고자 하였으며, 이때 사용된 endpoint로는 잎의 수, 잎 면적, 습중량, 건중량을 이용하였다. (중략)

• Speech Sciences
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• v.5 no.1
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• pp.81-88
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• 1999

This paper provides a method for speech detection. After first order FIR filtering on the speech signals, we applied the conventional method of endpoint detection which utilizes the energy as the criterion in separating signals from background noise. By FIR filtering, only the Fourier components with large values of [amplitude x frequency] become significant in energy profile. By applying this procedure to the 445-words database constructed from ETRI, we confirmed that the low-amplitude noise and/or the low-frequency noise are separated clearly from the speech signals, thereby enhancing the feasibility of ideal endpoint detections.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.75-76
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• 2001

An endpoint detection method for speech signals utilizing hidden Markov model(HMM) is proposed. It turns out that the proposed algorithm is quite satisfactory to apply isolated word speech recognition.

• ETRI Journal
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• v.36 no.5
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.