Abstract Deep reactive ion etching (DRIE) is critical for fabricating high-aspect-ratio structures in microelectromechanical systems (MEMS), yet its complex, parameter-dependent process poses significant optimization challenges. Artificial intelligence (AI) offers an efficient optimization solution, but its implementation faces the technical challenge of acquiring large-scale data from scanning electron microscopy (SEM) images, the standard for evaluating DRIE etching outcomes.
