Sequence modeling for predicting three-dimensional plasma etching profiles with deep learning

Three-dimensional plasma etching profile evolution remains a fundamental challenge in semiconductor process engineering, especially given its complexity and computational cost. In this work, we present a data-driven sequence modeling framework for efficient and accurate prediction of three-dimensional plasma etch profiles. A physically calibrated simulation model was used to generate a comprehensive dataset, encompassing over 2000 time-resolved vertical (y–z) profile sequences and 1000 depth-resolved lateral (x–y) pattern clips, covering a wide range of process conditions and layout geometries representative of modern optical proximity correction patterns. Building on this dataset, we develop a Conditional Convolutional Recurrent Neural Network designed to predict the temporal and spatial evolution of etching contours. The model achieves robust predictive accuracy, with an average Structural Similarity Index Measure above 0.94 across predicted frames, and demonstrates consistent performance for both simple and complex feature arrangements. The sequence modeling approach allows for rapid generation of full etch profile sequences, reducing computation time by orders of magnitude compared to traditional simulation workflows. This framework provides an efficient solution for data-driven plasma etch profile prediction, supporting potential applications in process monitoring, virtual metrology, and layout-level risk assessment.