Revolutionalize SEM Characterization with AI-Powered Image Analysis Tool

The Scanning Electron Microscope (SEM) is widely utilized in materials science and battery design research due to its high resolution and user-friendly operation. However, conventional SEM image analysis software requires substantial manual effort, making it highly susceptible to random noise and human bias. To address these limitations, we introduce Uni-AIMS (AI-Powered Microscopy Imaging System), an advanced automated software for SEM image analysis. Uni-AIMS leverages a cycle generative adversarial network (CycleGAN) architecture to eliminate the high annotation costs typical of deep learning methods, achieving state-of-the-art performance in characterization analysis with only minimal data. It has demonstrated significant efficacy in particle size analysis of NCM ternary lithium batteries and in alloy metallographic analysis. To further enhance accessibility, Uni-AIMS is packaged as an automated toolbox with an intuitive user interface and end-to-end analysis capabilities. Additionally, it can be easily adapted to other characterization applications through fine-tuning on a small set of SEM images.