Precise Pattern Alignment for Die-to-Database Inspection Based on the Generative Adversarial Network

Abstract

Alignment between the reference layout (or target pattern) and the corresponding scanning electron microscope (SEM) image is a crucial task for the die-to-database (D2DB) inspection in the semiconductor industry. However, it is challenging to align them accurately because the style and quality of reference layouts represented as a computer-aided design (CAD) are quite different from those of grayscale SEM images with noise. Direct application of conventional cross-correlation-based matching methods often leads to misalignment. Here, we propose a new method enabling the precise pattern alignment. The main idea is to transform SEM images into target-like CAD images using a generative adversarial network (GAN). As the generated and real layout images have similar style and quality, they can be precisely aligned using conventional matching methods. Then, the SEM image can be located correctly on the corresponding reference layout for inspection. A polygon-based clustering algorithm for target patterns is developed to avoid manual selection and minimize the number of training data.