Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data

TL;DR

This paper demonstrates that synthetic SEM images can effectively train deep learning models, like YOLOv8, for defect detection in photolithography, achieving high accuracy even on real SEM data.

Contribution

The study introduces a synthetic data generation approach for training defect detection models, enabling effective detection of very small defects in semiconductor manufacturing.

Findings

YOLOv8 achieved 96% mean average precision on synthetic data.

YOLOv8 detected 84.6% of Bridge defects on real SEM data.

Synthetic data can substitute real data for training robust defect detection models.

Abstract

In the photolithographic process vital to semiconductor manufacturing, various types of defects appear during EUV pattering. Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality data with good representation of smaller defects has prohibited deployment of deep learning based defect detection models in fabrication lines. To resolve the problem of data unavailability, we artificially generate scanning electron microscopy (SEM) images of line patterns with known distribution of defects and autonomously annotate them. We then employ state-of-the-art object detection models to investigate defect detection performance as a function of defect size, much smaller than the pitch width. We find that the real-time object detector YOLOv8 has the best mean average precision of 96% as compared to EfficientNet, 83%, and SSD, 77%, with the ability to detect smaller defects. We report the smallest defect size that can be detected reliably. When tested on real SEM data, the YOLOv8 model correctly detected 84.6% of Bridge defects and 78.3% of Break defects across all relevant instances. These promising results suggest that synthetic data can be used as an alternative to real-world data in order to develop robust machine-learning models.