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“Healthy China 2030”initiative has increasingly highlighted the role of university campuses in health promotion.However,many Chinese universities lack awareness of health-supportive environments,resulting in spat...The“Healthy China 2030”initiative has increasingly highlighted the role of university campuses in health promotion.However,many Chinese universities lack awareness of health-supportive environments,resulting in spatial systems that fail to meet the diverse physical and mental health needs of their communities.Among campus environments,outdoor public spaces are pivotal due to their openness and accessibility,making them ideal for fostering the autonomous health behaviors of students.This study investigates the optimization of these spaces to create environments that actively support well-being.Grounded in catalyst and affordance theories,we first identified the spatial preferences of students through questionnaire surveys.Subsequently,we employed the Delphi method and the Analytic Hierarchy Process(AHP)to construct an evaluation index system for healthy outdoor public spaces.This analysis revealed four core components:functional organization,perceptual environment,health-supportive facilities,and walking trails.For each component,we propose specific design principles and strategies,culminating in a spatial optimization framework aimed at promoting healthy behaviors.This study provides a theoretical foundation and practical guidance for designing health-oriented campus spaces and offers a replicable model for developing supportive university environments.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.
基金The Scientific Research Foundation of the Liaoning Provincial Department of Education(JYTMS20230414,J2020074)。
文摘The“Healthy China 2030”initiative has increasingly highlighted the role of university campuses in health promotion.However,many Chinese universities lack awareness of health-supportive environments,resulting in spatial systems that fail to meet the diverse physical and mental health needs of their communities.Among campus environments,outdoor public spaces are pivotal due to their openness and accessibility,making them ideal for fostering the autonomous health behaviors of students.This study investigates the optimization of these spaces to create environments that actively support well-being.Grounded in catalyst and affordance theories,we first identified the spatial preferences of students through questionnaire surveys.Subsequently,we employed the Delphi method and the Analytic Hierarchy Process(AHP)to construct an evaluation index system for healthy outdoor public spaces.This analysis revealed four core components:functional organization,perceptual environment,health-supportive facilities,and walking trails.For each component,we propose specific design principles and strategies,culminating in a spatial optimization framework aimed at promoting healthy behaviors.This study provides a theoretical foundation and practical guidance for designing health-oriented campus spaces and offers a replicable model for developing supportive university environments.
