Generation of microscopic structure of solder material with desirable characteristics based on deep learning

Abstract

Understanding the relationship between material characteristics and microscopic structure is important for the development of solder materials. To clarify this relationship, machine learning approaches are often used to predict material characteristics from microstructural images. Although a trained machine learning model can predict the material characteristics for a given microstructural image, it cannot directly create microstructural images of solder materials with desirable characteristics. Therefore, it is difficult to use machine learning to develop new solder materials. This paper presents a method for generating electron probe micro-analyzer (EPMA) images of a solder with desirable characteristics using deep learning. Our method uses a generative adversarial network (GAN) to generate images, and a convolutional neural network (CNN)-based evaluator to predict their characteristics. A common difficulty in applying machine learning to material science is the lack of training data, which often results in predictions with low accuracy. To address the small dataset problem, we trained the ranking prediction model of the characteristics instead of the regression model. Moreover, we employed transfer learning, in which a CNN model trained on texture datasets was used as the initial model. The experimental results show that the GAN successfully generated EPMA images that were similar to the actual images. The use of the ranking prediction model and transfer learning improved the performance of the CNN-based characteristic evaluator. We then selected promising generated EPMA images using the CNN-based characteristic evaluator and found that the characteristics of the selected EPMA images were consistent with expert experience.