The paper has focussed on the global landcover for the identification of cropland areas.Population growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production ne...The paper has focussed on the global landcover for the identification of cropland areas.Population growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human survival.Appropriate agricultural land monitoring requires proper management of land resources.The paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning techniques.The process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection process.The images are masked or annotated to produce the ground truth for the label identification of croplands,rivers,buildings,and backgrounds.The selected features are transferred to a machine learning model for the semantic segmentation process.The methodology has applied Random Forest,which has compared to two other techniques,Support Vector Machine and Multilayer perceptron,for the semantic segmentation process.Our dataset is composed of satellite images collected from the QGIS application.The paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99%training accuracy and 90%test accuracy.The results are cross-validated by computing the Mean loU and kappa coefficient that shows 93%and 69%score value respectively for Random Forest,found maximum among all.The paper has also calculated the area covered under the different segmented regions.Overall,Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping.展开更多
Opening the silicon oxide mask of a capacitor in dynamic random access memory is a critical process on a capacitive coupled plasma(CCP)etch tool.Three steps,dielectric anti-reflective coating(DARC)etch back,silicon ox...Opening the silicon oxide mask of a capacitor in dynamic random access memory is a critical process on a capacitive coupled plasma(CCP)etch tool.Three steps,dielectric anti-reflective coating(DARC)etch back,silicon oxide etch and strip,are contained.To acquire good performance,such as low leakage current and high capacitance,for further fabricating capacitors,we should firstly optimize DARC etch back.We developed some experiments,focusing on etch time and chemistry,to evalu-ate the profile of a silicon oxide mask,DARC remain and critical dimension.The result shows that etch back time should be con-trolled in the range from 50 to 60 s,based on the current equipment and condition.It will make B/T ratio higher than 70%mean-while resolve the DARC remain issue.We also found that CH_(2)F_(2) flow should be~15 sccm to avoid reversed CD trend and keep in-line CD.展开更多
文摘The paper has focussed on the global landcover for the identification of cropland areas.Population growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human survival.Appropriate agricultural land monitoring requires proper management of land resources.The paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning techniques.The process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection process.The images are masked or annotated to produce the ground truth for the label identification of croplands,rivers,buildings,and backgrounds.The selected features are transferred to a machine learning model for the semantic segmentation process.The methodology has applied Random Forest,which has compared to two other techniques,Support Vector Machine and Multilayer perceptron,for the semantic segmentation process.Our dataset is composed of satellite images collected from the QGIS application.The paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99%training accuracy and 90%test accuracy.The results are cross-validated by computing the Mean loU and kappa coefficient that shows 93%and 69%score value respectively for Random Forest,found maximum among all.The paper has also calculated the area covered under the different segmented regions.Overall,Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping.
文摘Opening the silicon oxide mask of a capacitor in dynamic random access memory is a critical process on a capacitive coupled plasma(CCP)etch tool.Three steps,dielectric anti-reflective coating(DARC)etch back,silicon oxide etch and strip,are contained.To acquire good performance,such as low leakage current and high capacitance,for further fabricating capacitors,we should firstly optimize DARC etch back.We developed some experiments,focusing on etch time and chemistry,to evalu-ate the profile of a silicon oxide mask,DARC remain and critical dimension.The result shows that etch back time should be con-trolled in the range from 50 to 60 s,based on the current equipment and condition.It will make B/T ratio higher than 70%mean-while resolve the DARC remain issue.We also found that CH_(2)F_(2) flow should be~15 sccm to avoid reversed CD trend and keep in-line CD.
