基于失效物理(Physics of Failure,PoF)分析方法,提出了规范化的系统级封装(System in Package,SiP)产品的可靠性评价标准。完成了国内外基于失效物理的SiP可靠性评价方法的适用性分析,并利用计算机仿真技术和手段设计了包含模型构建、...基于失效物理(Physics of Failure,PoF)分析方法,提出了规范化的系统级封装(System in Package,SiP)产品的可靠性评价标准。完成了国内外基于失效物理的SiP可靠性评价方法的适用性分析,并利用计算机仿真技术和手段设计了包含模型构建、应力剖面分析、可靠性预计与寿命预测等在内的SiP可靠性评价总体方案,给出了基于失效物理的SiP可靠性评价标准中的核心内容,包括评价流程、工作内容及详细要求等。还探讨了评价方案在某型SiP实际产品中的应用情况,表明该方案有效性强且工程适用,与基于加速寿命试验的产品可靠性评价结果吻合度较高,研究成果有助于解决当期SiP可靠性评价缺乏统一有效方法、评价针对性差、寿命试验周期长、缺乏失效数据、试验成本高等难题。展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
文摘基于失效物理(Physics of Failure,PoF)分析方法,提出了规范化的系统级封装(System in Package,SiP)产品的可靠性评价标准。完成了国内外基于失效物理的SiP可靠性评价方法的适用性分析,并利用计算机仿真技术和手段设计了包含模型构建、应力剖面分析、可靠性预计与寿命预测等在内的SiP可靠性评价总体方案,给出了基于失效物理的SiP可靠性评价标准中的核心内容,包括评价流程、工作内容及详细要求等。还探讨了评价方案在某型SiP实际产品中的应用情况,表明该方案有效性强且工程适用,与基于加速寿命试验的产品可靠性评价结果吻合度较高,研究成果有助于解决当期SiP可靠性评价缺乏统一有效方法、评价针对性差、寿命试验周期长、缺乏失效数据、试验成本高等难题。
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
