Spatial batch optimal design based on self-learning Gaussian process models for LPCVD processes

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.

Characterizing changes in land cover and forest fragmentation from multitemporal Landsat observations(1993-2018)in the Dhorpatan Hunting Reserve,Nepal

Natural forces and anthropogenic activities greatly alter land cover,deteriorate or alleviate forest fragmentation and affect biodiversity.Thus land cover and forest fragmentation dynamics have become a focus of concern for natural resource management agencies and biodiversity conservation communities.However,there are few land cover datasets and forest fragmentation information available for the Dhorpatan Hunting Reserve(DHR)of Nepal to develop targeted biodiversity conservation plans.In this study,these gaps were filled by characterizing land cover and forest fragmentation trends in the DHR.Using five Landsat images between 1993 and 2018,a support vector machine algorithm was applied to classify six land cover classes:forest,grasslands,barren lands,agricultural and built-up areas,water bodies,and snow and glaciers.Subsequently,two landscape process models and four landscape metrics were used to depict the forest fragmentation situations.Results showed that forest cover increased from 39.4%in 1993 to 39.8%in 2018.Conversely,grasslands decreased from 38.2%in 1993 to 36.9%in 2018.The forest shrinkage was responsible for forest loss during the period,suggesting that the loss of forest cover reduced the connectivity between forest and nonforested areas.Expansion was the dominant component of the forest restoration process,implying that it avoided the occurrence of isolated forests.The maximum value of edge density and perimeter area fractal dimension metrics and the minimum value of aggregation index were observed in 2011,revealing that forests in this year were most fragmented.These specific observations from the current analysis can help local authorities and local communities,who are highly dependent on forest resources,to better develop local forest management and biodiversity conservation plans.