Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard ...Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.展开更多
The main propose of this research was to develop a two-step biodiesel production technique from animal fat as a raw material. The developed process consists ofesterification and transesterification steps. With special...The main propose of this research was to develop a two-step biodiesel production technique from animal fat as a raw material. The developed process consists ofesterification and transesterification steps. With special attention to optimize the first step is acid catalyzed esterification to reduce free fatty acid content and the second step is alkali catalyzed transesterification for converting triglyceride to fatty acid methyl ester or biodiesel. Animal fat containing 78.80 mg KOH/g of high acid value and molecular weight of 851 g/mol with the highest oleic acid content was used as raw material. Respond surface methodology （RSM） was applied for the experiment design. This were 20 experiments involving the three investigated variables of methanol to animal fat ratio, amount of sulfuric acid catalyst and reaction time that were studied on esterification to optimize the condition for decreasing acid value in animal fat less than 2 mg KOH/g. The animal fat with low acid value was further experimented in transesterification step to obtain fatty acid methyl ester or biodiesel. Animal fat biodiesel is further investigated by determining its fuel properties according to the ASTM standard test method.展开更多
基金Supported by the National High Technology Research and Development Program of China(2014AA041803)the National Natural Science Foundation of China(61320106009)
文摘Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.
文摘The main propose of this research was to develop a two-step biodiesel production technique from animal fat as a raw material. The developed process consists ofesterification and transesterification steps. With special attention to optimize the first step is acid catalyzed esterification to reduce free fatty acid content and the second step is alkali catalyzed transesterification for converting triglyceride to fatty acid methyl ester or biodiesel. Animal fat containing 78.80 mg KOH/g of high acid value and molecular weight of 851 g/mol with the highest oleic acid content was used as raw material. Respond surface methodology （RSM） was applied for the experiment design. This were 20 experiments involving the three investigated variables of methanol to animal fat ratio, amount of sulfuric acid catalyst and reaction time that were studied on esterification to optimize the condition for decreasing acid value in animal fat less than 2 mg KOH/g. The animal fat with low acid value was further experimented in transesterification step to obtain fatty acid methyl ester or biodiesel. Animal fat biodiesel is further investigated by determining its fuel properties according to the ASTM standard test method.
