AI enhances understanding of dendritic growth in thin films

Researchers at Tokyo University of Science have created a new artificial intelligence model to better understand dendritic growth in thin films. Thin films are very thin layers of materials used in technologies like semiconductors and communication systems. Dendritic growth refers to tree-like patterns that can form during the manufacturing process of these films, which can affect their quality and performance. These dendrites are problematic for producing large thin-film devices needed for commercial use. Understanding how and why these structures form is important to improve the thin-film growth process. Previous methods for studying dendritic growth have been limited and often required trial and error. The new AI model combines machine learning with a technique called persistent homology. This approach allows researchers to analyze the complex shapes of dendrites more accurately than traditional methods. The team looked at how changes in the dendrite structure relate to Gibbs free energy, which influences how dendrites form. By using this innovative method, researchers were able to map the characteristics of dendrites in relation to energy levels. The findings help reveal the conditions that impact dendritic branching. The researchers believe this could lead to high-quality thin films, which are essential for speeding up communication technologies beyond 5G. This new framework not only promises advancements in material science but may also contribute to developments in sensor technology and other complex systems.