Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

In order to renovate spirodiclofen contaminated soil through biodegradation, a high-effective spirodiclofen degrading bacterium was isolated from corn cultivated soil in Weifang of Shandong, China using enrichment culture and HPLC method. The strain identification was studied, the best cultivated conditions and pesticides degrading substrate range of the strain was also determined. The spirodiclofen degradation rate of the screened strain WF14-6 was 76.42% after 120 h cultivation, based on morphology and 16S rRNA gene sequence analysis, the strain was identified as Enterobacter sp. The best cultivated temperature and pH of the strain were 30℃and 7.0. After 72 h cultivation, the degradation rate of strain to imidacloprid and acifluorfene was 31.35~41.06%, the degradation rate to bupirimate、diafenthiuron and dimethomorph was 20.81~23.90%.

A framework for knowledge management system has been explored which enables the semantic search of knowledge on the web Knowledge representation by RDF and RDF schema enables machine cognition of knowledge documents. Dnblin core was adopted for structured

A framework for knowledge management system has been explored which enables the semantic search of knowledge on the web. Knowledge representation by RDF and RDF schema enables machine cognition of knowledge documents. Dublin core was adopted for structured metadata representation. Thereby, it enables the CBR and rule based reasoning for intelligent knowledge retrieval. Grafting of the WFMS technique unto the KMS facilitates the effective utilization of process knowledge and creation of new knowledge.

Conatact barrier metal용 selective W CVD 기술에서 SiH4//WF6(=R)유량비가 W막의 비저항, contact resistance, 접합주설전류 등의 전기적 특성에 미치는 영향을 β-W 의 생성에 촛점을 맞추어 조사하였다. R의 증가에 따라 W의 비저항이 증가하는데, 그 주원인은 α-W로 부터 β-W 로의 상변태에 있다. SiH4환원에 의한 CVD W에서 생성되는 β-W 는 산소에 의해서가 아니라 막내에 유입된 Si에 의하여 안정화된다. Si기탄상에 W를 증착할 때에는 R값이 클 경우에 β-W 가 생성되지만, TiN 기판상에 W를 증착할 때에는 R값이 큰 경우에도 β-W 가 생성되지 않는 것으로 나타났다. 또한 R이 증가함에 따라 접합누설전류가 증가하는데, 이것은 W-Si계면에 대한 수직방향으로의 Si의 소모뿐만 아니라 수평방향으로의 Si의 소모에도 그 원인이 있는 것으로 보인다.

WF6와 SiH4의 화학반옹으로부터 산화규소막 위에 텅스텐 핵이 형성되는 현상을 실험을 통해 관찰하였다. 핵이 생성되는 속도는 반응온도가 높고 운반기체의 유량이 적으며 반응기내의 압력이 높을수록 큰 것으로 나타났다. 또한 반응기체가 흘러가는 방향에서 아랫쪽으로 위치하는 표면에 핵이 생성되는 속도가 큰 것으로 나타났다. 산화막위에 생성된 텅스텐 핵의 형상과 파단면을 주사현미경으로 관찰하였으며 산화막위에 형성된 텅스텐 박막의 화학적 조성을 밝혀내었다.