Combining Full Wafer Inspection with Deep Learning to Recognize Wafers with Critical Defects

The paper will present an approach to automate and accelerate detection of defective wafers in high-volume manufacturing by innovatively complementing fast, non-contact and non-destructive photoluminescence wafer mapping with smart image classification using deep learning methods. Specific focus is on distinguishing wafers with regularly occurring defect patterns in the measurement from those revealing atypical patterns from buried, electrically active defects, which are hard to detect by visual inspection of the photoluminescence map. The latter should be reliably and automatically identified after the measurement, thus preventing decrease of production reliability or even product failure through the introduction of further measures on the affected wafers.This task has been treated as an image classification problem for which neural networks-based algorithms were developed. On the one hand, these algorithms deliver promising classification results for the use-case specific wafer maps that are ready for critical assessment by process experts and for comparison to other defect identification techniques. On the other hand, the developed algorithms were verified to perform well on commonly available reference data.

[1]  J. Tortai,et al.  Full Wafer Process Control Through Object Detection Using Region-Based Convolutional Neural Networks , 2022, 2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC).

[2]  J. Jeong,et al.  Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps , 2022, Applied Sciences.

[3]  Zemenu Endalamaw Amogne,et al.  A Deep convolutional neural network with residual blocks for wafer map defect pattern recognition , 2021, Qual. Reliab. Eng. Int..

[4]  Chao Duan,et al.  Segmentation of Cracked Silicon Wafer Image Based on Deep Learning , 2021, Journal of Physics: Conference Series.

[5]  Bedir Tekinerdogan,et al.  Machine learning applications in production lines: A systematic literature review , 2020, Comput. Ind. Eng..

[6]  Muhammad Saqlain,et al.  A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes , 2020, IEEE Transactions on Semiconductor Manufacturing.

[7]  Chun-Chin Hsu,et al.  Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks , 2020, Symmetry.

[8]  Mohd Ibrahim Shapiai,et al.  Convolutional Neural Network for Imbalanced Data Classification of Silicon Wafer Defects , 2020, 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA).

[9]  Seokho Kang,et al.  Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks , 2020, IEEE Access.

[10]  Takeshi Nakazawa,et al.  Anomaly Detection and Segmentation for Wafer Defect Patterns Using Deep Convolutional Encoder–Decoder Neural Network Architectures in Semiconductor Manufacturing , 2019, IEEE Transactions on Semiconductor Manufacturing.

[11]  J. Baruchel,et al.  Photoluminescence Imaging for Buried Defects Detection in Silicon: Assessment and Use-Cases , 2019, IEEE Transactions on Semiconductor Manufacturing.

[12]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Allan Hanbury,et al.  Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool , 2015, BMC Medical Imaging.

[15]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  Dohyun Baek.  Properties of Silicon for Photoluminescence , 2014 .

[18]  W. Yoo,et al.  Ultra-Thin SiO2/Si Interface Quality In-Line Monitoring Using Multiwavelength Room Temperature Photoluminescence and Raman Spectroscopy , 2014 .

[19]  Ting-Chun Wang,et al.  Visualization of Plasma Etching Damage of Si Using Room Temperature Photoluminescence and Raman Spectroscopy , 2013 .

[20]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[21]  S. Rouvimov,et al.  Photoluminescence Intensity Analysis in Application to Contactless Characterization of Silicon Wafers , 2003 .