# Optimizing YOLOv7 for Semiconductor Defect Detection

arXiv: https://arxiv.org/abs/2302.09565 · HTML: https://mlsanity.com/paper/2302.09565/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [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)

- [YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach](https://mlsanity.com/paper/2307.15516/) (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)
- [Improved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy](https://mlsanity.com/paper/2311.11439/) (2023)
- [Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization](https://mlsanity.com/paper/2311.11145/) (2023)
- [Consensus Focus for Object Detection and minority classes](https://mlsanity.com/paper/2401.05530/) (2024)
- [Advancing SEM Based Nano-Scale Defect Analysis in Semiconductor Manufacturing for Advanced IC Nodes](https://mlsanity.com/paper/2409.04310/) (2024)
- [BoardVision: Deployment-ready and Robust Motherboard Defect Detection with YOLO+Faster-RCNN Ensemble](https://mlsanity.com/paper/2510.14389/) (2025)

## Abstract (arXiv)

The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. YOLOv7 is a state-of-the-art object detector based on the YOLO family of models which have become popular for industrial applications. One such possible application domain can be semiconductor defect inspection. The performance of any machine learning model depends on its hyperparameters. Furthermore, combining predictions of one or more models in different ways can also affect performance. In this research, we experiment with YOLOv7, a recently proposed, state-of-the-art object detector, by training and evaluating models with different hyperparameters to investigate which ones improve performance in terms of detection precision for semiconductor line space pattern defects. The base YOLOv7 model with default hyperparameters and Non Maximum Suppression (NMS) prediction combining outperforms all RetinaNet models from previous work in terms of mean Average Precision (mAP). We find that vertically flipping images randomly during training yields a 3% improvement in the mean AP of all defect classes. Other hyperparameter values improved AP only for certain classes compared to the default model. Combining models that achieve the best AP for different defect classes was found to be an effective ensembling strategy. Combining predictions from ensembles using Weighted Box Fusion (WBF) prediction gave the best performance. The best ensemble with WBF improved on the mAP of the default model by 10%.