# SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection

arXiv: https://arxiv.org/abs/2308.07180 · HTML: https://mlsanity.com/paper/2308.07180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning](https://mlsanity.com/paper/2208.03514/) (2022)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [Deep Learning-Based Defect Classification and Detection in SEM Images](https://mlsanity.com/paper/2206.13505/) (2022)
- [Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach](https://mlsanity.com/paper/2211.02185/) (2022)
- [Optimizing YOLOv7 for Semiconductor Defect Detection](https://mlsanity.com/paper/2302.09565/) (2023)
- [WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects](https://mlsanity.com/paper/2207.00960/) (2022)

## Built on by (most cited first, 3 of 3)

- [Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network](https://mlsanity.com/paper/2411.11029/) (2024)
- [Advancing SEM Based Nano-Scale Defect Analysis in Semiconductor Manufacturing for Advanced IC Nodes](https://mlsanity.com/paper/2409.04310/) (2024)
- [LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds](https://mlsanity.com/paper/2608.03078/) (2026)

## Abstract (arXiv)

Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like KNN mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method.