Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach

TL;DR

This paper applies Mask R-CNN, a deep learning model, to improve defect detection and segmentation in SEM images for semiconductor manufacturing, enabling precise defect instance identification and quantification.

Contribution

The work extends previous deep learning methods to achieve accurate defect instance segmentation and quantification in SEM images, addressing challenges in high-resolution semiconductor defect inspection.

Findings

Effective defect segmentation demonstrated quantitatively and qualitatively

Able to differentiate between multiple defect categories and intra-class variations

Enables precise defect counting and surface area measurement

Abstract

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.