Process-Aware Digital Twins by Deep Learning for DUV Photolithography and Plasma Etch

Abstract

Computer representations of the structure, context, and behavior of physical systems are critical components of computational system optimization. Traditionally, such optimization is done by iterative physical experiments, which can be expensive both in time and resources. In this paper, these computer representations, called digital twins, are developed primarily using SEM images and equipment process parameters. HyperPix2Pix, the proposed methodology of the digital twins, is a deep neural network that uses SEM images of the input structure together with equipment process parameters to predict the output SEM images. We demonstrate HyperPix2Pix on a DUV photolithography stepper and plasma etcher. HyperPix2Pix predicts output images that closely match the experimental output images and have very similar critical dimensions. Compared to previous work, HyperPix2Pix includes the effects of process parameters through multimodal learning, elucidating the role of different parameters in nanofabrication processes and their effects on critical dimensions of the resulting structures.