AFM Imaging Defect Detection and Classification Using Deep Learning

Abstract

Atomic Force Microscopy (AFM) has been a broadly used platform for high-resolution imaging and mechanical characterization of a wide range of samples. However, this technique can be time-consuming and heavily relies on constant human supervision and human insight for data acquisition and analysis. Recent advancement in artificial intelligence (AI) provides the potential for efficient data analysis for AFM applications. The fusion of AFM with AI for effective image analysis and classification still remains an ongoing research endeavor. In this study, we present a novel AFM image defect detection and classification framework, AFM_YOLO-ResNet, using advanced deep learning (DL) techniques. Central to our approach is a highly integrated DL model that consists of a YOLO image defect detection layer and a ResNet feature extraction and classification layer. The proposed AFM_YOLO-ResNet framework is trained with expert annotated AFM images, and prepared to assess future AFM images from similar samples. Performance of AFM_YOLO-ResNet was validated for AFM image defect detection and classification, and compared with three commonly used transfer learning and computer vision models (Googlenet, Darknet, and YOLOv8). The results with high training and validation accuracies demonstrated that the AFM_YOLO-ResNet framework greatly improves the AFM imaging analysis efficiency.

Keywords

• AFM
• deep learning
• image analysis
• identification
• classification