TCAD augmented generative adversarial network for hot-spot detection and mask-layout optimization in a large area HARC etching process

Abstract

Cost-effective vertical etching of plug holes and word lines is crucial in enhancing 3D NAND device manufacturability. Even though multiscale technology computer-aided design (TCAD) methodology is suitable for effectively predicting etching processes and optimizing recipes, it is highly time-consuming. This article demonstrates that our deep learning platform called TCAD-augmented Generative Adversarial Network can reduce the computational load by 2 600 000 times. In addition, because well-calibrated TCAD data based on physical and chemical mutual reactions are used to train the platform, the etching profile can be predicted with the same accuracy as TCAD-only even when the actual experimental data are scarce. This platform opens up new applications, such as hot spot detection and mask layout optimization, in a chip-level area of 3D NAND fabrication. Published under an exclusive license by AIP Publishing.