@article{oai:kyutech.repo.nii.ac.jp:00007273,
 author = {Watanabe, Akihiko and 渡邉, 晃彦 and Hirose,  Naoto and Kim, Hyungseop and 金, 亨燮 and Omura, Ichiro and 大村, 一郎},
 journal = {Microelectronics Reliability},
 month = {Sep},
 note = {An image diagnosis by deep learning was applied to failure analysis of power devices. A series of images during a process to failure by power cycling test was used for this method. The images were obtained by a scanning acoustic microscopy of our real-time monitoring system. An image classifier was designed based on a convolutional neural network (CNNs). A developed classifier successfully diagnosed input image into a normal device and an abnormal device. The accuracy of classification was improved by introducing a pre-training and an overlapping pooling into the system. A technique to extract a feature related a failure is essential for the failure analysis based on the real-time monitoring and the deep learning is one likely candidate for it.},
 pages = {113399-1--113399-5},
 title = {Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices},
 volume = {100-101},
 year = {2019},
 yomi = {ワタナベ, アキヒコ and オオムラ, イチロウ}
}