End-to-End Lithography Modeling Based on Process Parameters and Deep Learning

Lithography is one of the most important processes in integrated circuit manufacturing, and with the continuous advancement of technology nodes, the cost of computational lithography is also increasing. Previous studies have mostly viewed lithography system as a black box mapping from image to image, lacking guidance on process parameters and supporting experimental data when comparing experimental results. This article models the lithography system based on process parame-ters and deep learning, and verifies it using experimental data. The optical system combines process parameters and uses sum of coherent systems (SOCS) to reduce the computational complexity of optical simulation. The resist system takes aerial images as input and outputs binary masks. Our proposed method has a 9.36% accuracy improvement in mean intersection over union (mIOU) compared to traditional compact resist models.