A power cycling degradation inspector of power semiconductor devices

We have proposed a failure analysis based on a real-time monitoring of power devices under acceleration test. The real-time monitoring enables to visualize the mechanism that leads to a failure by obtaining the change of structure inside the device in time domain with high spatial resolution. In this paper, we presented a new analytical instrument based on the proposed failure analysis concept. The essential functions of this instrument are (1) power stress control, (2) non-destructive inspection and (3) water circulation. An original design power-stress control system and a customized scanning acoustic microscopy system enable us a non-destructive inspection inside the device under power cycling test. This instrument exhibits a great advantage especially to monitor failure mechanisms without having to open the module

Determination of Abnormality of IGBT Images Using VGG16

A power device is a semiconductor device for power control used for power conversion such as converting direct current to alternating current and alternating current to direct current. It is widely used such as refrigerators, air conditioners which is implemented electronic components that are closely related to our daily lives. Therefore, high reliability and safety are required, and power cycle tests are conducted for the purpose of evaluating them. In the conventional test, there is a problem that it is difficult to perform analysis because sparks are generated during the test and the device is severely damaged after the test. To solve this problem, a new technology has been developed that adds ultrasonic that enable internal observation during the test. However, there are remains a problem that the method for analyzing the ultrasonic image obtained in the new technology has not been established. Also, few abnormal images are obtained in the test. In this paper, we propose a method for detection of abnormal devices based on CNN. Especially, we implement a Cycle-GAN to extend the abnormal data and classify the known image based on improved VGG16. As an experimental result, classification accuracy of Precision = 97.06%, Recall = 93.58%, F - measure = 95.17% were obtained.21th International Conference on Control, Automation and Systems, ICCAS 2021, October 12-15, 2021, Jeju, Korea and onlin

Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices

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

A power cycling degradation inspector of power semiconductor devices

We have proposed a failure analysis based on a real-time monitoring of power devices under acceleration test. The real-time monitoring enables to visualize the mechanism that leads to a failure by obtaining the change of structure inside the device in time domain with high spatial resolution. In this paper, we presented a new analytical instrument based on the proposed failure analysis concept. The essential functions of this instrument are (1) power stress control, (2) non-destructive inspection and (3) water circulation. An original design power-stress control system and a customized scanning acoustic microscopy system enable us a non-destructive inspection inside the device under power cycling test. This instrument exhibits a great advantage especially to monitor failure mechanisms without having to open the module