University of Twente Student Theses
Reconstruction-based Anomaly Detection with Machine Learning for High Throughput Scanning Electron Microscope Defect Inspection
Tian, X. (2022) Reconstruction-based Anomaly Detection with Machine Learning for High Throughput Scanning Electron Microscope Defect Inspection.
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| Full Text Status: | Access to this publication is restricted |
| Embargo date: | 31 August 2026 |
| Abstract: | With rapid advancement of high throughput wafer inspection systems, there are growing demands on more efficient and accurate defect inspection algorithms. This work focuses on investigating the feasibility of applying learning based algorithms to the image processing pipeline of the next generation wafer inspection tools. An end-to-end reconstruction-based anomaly detection methodology is proposed with two types of reconstruction models based on Principal Component Analysis and Convolutional Autoencoder. Both methods have demonstrated competitive performance compared to the current defect detection algorithm on multiple datasets with different patterns and various defect types. |
| Item Type: | Essay (Master) |
| Clients: | ASML Delft, Delft, Netherlands |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Embedded Systems MSc (60331) |
| Link to this item: | https://purl.utwente.nl/essays/92906 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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