Estimation of low-cycle fatigue damage of sputtered Cu thin films at the micro scale using deep learning

Abstract

Herein, we propose a method to estimate fatigue damage of a thin metal film at the micro scale with high accuracy. With progress in the miniaturization of flexible printed circuits, estimation of damage at the micro scale is required to improve the reliability of industrial processing. Despite this necessity, it has been difficult to accurately evaluate the damage at the micro scale using conventional tests. Therefore, we focus on the use of microscopic images for damage estimation based on the correlation between damage and surface morphology, such as cracks. However, image-based analytical estimation by physical modeling is extremely difficult because of the complexity of the morphology. Therefore, the proposed method is based on image recognition using deep learning. In particular, VGG19-based transfer learning was implemented. An L2-constrained softmax loss developed for face recognition was used, as the diversity of textures in the microscopic images was lower than that used for general object detection. As a result, the proposed method was able to accurately estimate the fatigue damage at the micro scale, which was at the submillimeter scale. The average estimation error was reduced to 15% of that obtained using the binarization method, indicating the L2-constrained softmax loss method to be highly effective. Although verification under a broad range of fatigue conditions is required for a more general evaluation, it was concluded that the proposed method is effective for evaluating the damage of thin metal films of flexible printed circuits at the micro scale.