TEMPO: Fast Mask Topography Effect Modeling with Deep Learning

Abstract

With the continuous shrinking of the semiconductor device dimensions, mask topography effects stand out among the major factors influencing the lithography process. Including these effects in the lithography optimization procedure has become necessary for advanced technology nodes. However, conventional rigorous simulation for mask topography effects is extremely computationally expensive for high accuracy. In this work, we propose TEMPO as a novel generative learning-based framework for efficient and accurate 3D aerial image prediction. At its core, TEMPO comprises a generative adversarial network capable of predicting aerial image intensity at different resist heights. Compared to the default approach of building a unique model for each desired height, TEMPO takes as one of its inputs the desired height to produce the corresponding aerial image. In this way, the global model in TEMPO can capture the shared behavior among different heights, thus, resulting in smaller model size. Besides, across-height information sharing results in better model accuracy and generalization capability. Our experimental results demonstrate that TEMPO can obtain up to 1170x speedup compared with rigorous simulation while achieving satisfactory accuracy.