AI Optimizes Deep Reactive Ion Etching

A new study introduces a physics-constrained variational autoencoder (VAE) that leverages AI to recognize and optimize critical features in scanning electron microscope (SEM) profiles during deep reactive ion etching (DRIE) processes. This approach improves both the speed and accuracy of verification in complex silicon structures, paving the way for higher-yield, lower-defect ASICs.