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

arXiv: https://arxiv.org/abs/2211.02185 · HTML: https://mlsanity.com/paper/2211.02185/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Deep Learning-Based Defect Classification and Detection in SEM Images](https://mlsanity.com/paper/2206.13505/) (2022)

## Built on by (most cited first, 8 of 8)

- [Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images](https://mlsanity.com/paper/2407.10348/) (2024)
- [SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering](https://mlsanity.com/paper/2302.09569/) (2023)
- [SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation](https://mlsanity.com/paper/2307.08693/) (2023)
- [SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection](https://mlsanity.com/paper/2308.07180/) (2023)
- [Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review](https://mlsanity.com/paper/2308.08376/) (2023)
- [Advancing SEM Based Nano-Scale Defect Analysis in Semiconductor Manufacturing for Advanced IC Nodes](https://mlsanity.com/paper/2409.04310/) (2024)
- [Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation](https://mlsanity.com/paper/2603.14282/) (2026)
- [LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds](https://mlsanity.com/paper/2608.03078/) (2026)

## Abstract (arXiv)

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.