• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.247-251
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• 2004

반도체 소자의 제조에 있어 실리콘 표면에 성장한 자연산화막을 제거하기 위해 일반적으로 습식 세정 기술이 이용되어 왔다. 하지만 소자의 최소 선폭(design rule)이 nano급으로 고집적화 됨에 따라 contact hole 바닥의 자연산화막을 깨끗이 제거하는데 있어서 그 한계를 나타나고 있다. 이에 대한 효과적인 대안 공정으로 가스 건식 세정 기술이 연구되고 있다. 본 논문에서는 한 번에 50매 이상의 웨이퍼를 처리함으로써 생산성 측면에서 월등한 배치식 설비에서 원거리 플라즈마(remote plasma) 장치에서 2.450Hz의 마이크로웨이브(${\mu}$-wave)에 의해 형성시킨 수소라디칼과 $NF_3$ 가스를 이용하여 실리콘에 결함을 주지 않고 자연산화막을 선택적으로 제거하는 공정에 대해 고찰하였다. AFM을 이용한 표면분석, TEM을 이용한 물성분석, 그리고 ToF-SIMS 및 XPS를 이용한 화학 분석을 습식 및 건식 세정을 비교 평가한 결과, 건식 세정 공정이 실리콘 표면에 결함을 주지 않고 자연산화막을 제거 할 수 있음을 확인하였다. 산화막$(SiO_2)$, 질화막$(Si_3N_4)$, 그리고 다결정 실리콘(Poly-Si) 등의 각 막질별 식각 특성을 고찰하였으며, $NH_3$의 캐리어 가스인 $N_2$의 주입량을 조절함으로써 수소라디칼 형성 효율의 개선이 가능하였으며, 이로부터 게이트와 소스/드레인 사이를 절연하기 위해 이용되는 질화막의 식각 선택비를 2배 정도 개선할 수 있었다. nano급 소자에 실장하여 평가한 결과에서 불산(HF)에 의한 습식 세정 방식에 비하여 약 $20{\sim}50%$ 정도의 contact 저항 감소 효과가 있음이 확인되었다.두 소자 모두 $40mA/cm^2$ 에서 이상적인 화이트 발란스와 같은(0.33,0.33)의 색좌표를 보였다.epsilon}_0=1345$의 빼어난 압전 및 유전특성과 $330^{\circ}C$의 높은 $T_c$를 보였고 그 조성의 vibration velocity는 약4.5 m/s로 나타났다.한 관심이 높아지고 있다. 그러나 고 자장 영상에서의 rf field 에 의한 SAR 증가는 중요한 제한 요소로 부각되고 있다. 나선주사영상은 SAR 문제가 근원적으로 발생하지 않고, EPI에 비하여 하드웨어 요구 조건이 낮아 고 자장에서의 고속영상방법으로 적합하다. 본 논문에서는 고차 shimming 을 통하여 불균일도를 개선하고, single shot 과 interleaving 을 적용한 multi-shot 나선주사영상 기법으로 $100{\times}100$에서 $256{\times}256$의 고해상도 영상을 얻어 고 자장에서 초고속영상기법으로 다양한 적용 가능성을 보였다. 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유의성이 움직임 보정 전에 비하여 낮음을 알 수 있었다. 결론: 뇌활성화 과제 수행시에 동반되는 피험자의 머리 움직임에 의하여 도파민 유리가 과대평가되었으며 이는 이 연구에서 제안한 영상정합을 이용한 움직임 보정기법에 의해서 개선되었다. 답이 없는 문제, 문제 만들기, 일반화가 가능한 문제 등으로 보고, 수학적 창의성 중 특히 확산적 사고에 초점을 맞추어 개방형 문제가 확

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.249-254
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• 2022

With the rapid advance of the semiconductor and Information and communication technologies, remote environment monitoring technology, which can detect and analyze surrounding environmental conditions with various types of sensors and wireless communication technologies, is also drawing attention. However, since the conventional remote environmental monitoring systems require external power supplies, it causes time and space limitations on comfortable usage. In this study, we proposed the concept of the self-powered remote environmental monitoring system by supplying the power with the levitation-electromagnetic generator (L-EMG), which is rationally designed to effectively harvest biomechanical energy in consideration of the mechanical characteristics of biomechanical energy. In this regard, the proposed L-EMG is designed to effectively respond to the external vibration with the movable center magnet considering the mechanical characteristics of the biomechanical energy, such as relatively low-frequency and high amplitude of vibration. Hence the L-EMG based on the fragile force equilibrium can generate high-quality electrical energy to supply power. Additionally, the environmental detective sensor and wireless transmission module are composed of the micro control unit (MCU) to minimize the required power for electronic device operation by applying the sleep mode, resulting in the extension of operation time. Finally, in order to maximize user convenience, a mobile phone application was built to enable easy monitoring of the surrounding environment. Thus, the proposed concept not only verifies the possibility of establishing the self-powered remote environmental monitoring system using biomechanical energy but further suggests a design guideline.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.