딥 러닝 회귀 모델 기반의 TSOM 계측

A Through-focus Scanning Optical Microscopy Dimensional Measurement Method based on a Deep-learning Regression Model

  • 투고 : 2022.03.07
  • 심사 : 2022.03.25
  • 발행 : 2022.03.31

초록

The deep-learning-based measurement method with the through-focus scanning optical microscopy (TSOM) estimated the size of the object using the classification. However, the measurement performance of the method depends on the number of subdivided classes, and it is practically difficult to prepare data at regular intervals for training each class. We propose an approach to measure the size of an object in the TSOM image using the deep-learning regression model instead of using classification. We attempted our proposed method to estimate the top critical dimension (TCD) of through silicon via (TSV) holes with 2461 TSOM images and the results were compared with the existing method. As a result of our experiment, the average measurement error of our method was within 30 nm (1σ) which is 1/13.5 of the sampling distance of the applied microscope. Measurement errors decreased by 31% compared to the classification result. This result proves that the proposed method is more effective and practical than the classification method.

키워드

참고문헌

  1. V. Vartanian, R. Attota, H. Park, G. Orji, R. A. Allen, "TSV reveal height and dimension metrology by the TSOM method" Proc. SPIE 8681, 10.1117/12.2012609, 2012
  2. R. Attota, R. G. Dixson, J. A. Kramar, J. E. Potzick, A. E. Vladar, B. Bunday, E. Novak, A. Rudack, "TSOM method for semiconductor metrology", Proc. SPIE 7971, doi: 10.1117/12.881620, 2011
  3. R. Attota, R. Silver, and R. Dixson, "Linewidth measurement technique using through-focus optical images," Appl. Opt. 47(4), 495-503 (2008). https://doi.org/10.1364/AO.47.000495
  4. S. Usha, P. V Shashikumar, G. C. Mohankumar, and S. S. Rao, "Through Focus Optical Imaging Technique To Analyze Variations In Nano-Scale Indents," J. Biomed. Opt. 23(07), 1-100 (2018).
  5. Y. Qu, J. Hao, and R. Peng, "Machine-learning models for analyzing TSOM images of nanostructures," Opt. Express 27(23), 33978 (2019). https://doi.org/10.1364/oe.27.033978
  6. H Nie, R Peng, J Ren, Y Qu, "A through-focus scanning optical microscopy dimensional measurement method based on deep-learning classification model," Journal of Microscopy, 2021
  7. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778). Piscataway, New Jersey: IEEE.
  8. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4700-4708). Piscataway, New Jersey: IEEE.
  9. Arceo, A., Bunday, B., and Attota, R., "Use of TSOM for sub-11nm node pattern defect detection and HAR features," Proc. SPIE 8681, 86812G (2013)
  10. Lee, J. H., Park, J. H., Jeong, D., Shin, E. J. and Park, C., "Tip/tilt-compensated through-focus scanning optical microscopy," Proc. SPIE 10023, 100230P (2016).
  11. http://www.nextinsol.com/
  12. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
  13. Selvaraju, Ramprasaath R., Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization." In ICCV, pp. 618-626. 2017
  14. Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation. arXiv preprint arXiv:1810.13243, 2018.
  15. Kingma, Diederik P and Ba, Jimmy Lei. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  16. Siddharth Mahendran, Haider Ali, and Rene Vidal. A mixed classification-regression framework for 3D pose estimation from 2D images. In British Machine Vision Conference (BMVC), 2018.
  17. Z. Niu, M. Zhou, L. Wang, X. Gao, G. Hua, Ordinal regression with multiple output cnn for age estimation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4920-4928.
  18. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR (2016)
  19. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al, "Imagenet large scale visual recognition challenge," International Journal of Computer Vision, Vol 15, No 3, pp. 211-252, 2015
  20. Sung Joo Kim, and Kim Gyung Bum, "A Study on the Classification of Surface Defect Based on Deep Convolution Network and Transfer-learning," Journal of the Semiconductor & Display Technology, Vol. 20, No. 1. March 2021.
  21. Sung-jin Hwang, and Seok-woo Hong, Jong-seo Yoon, Heemin Park, Hyun-chul Kim, "Deep Learning-based Pothole Detection System," Journal of the Semiconductor & Display Technology, Vol. 20, No. 1. March 2021.
  22. Song-Yeon Lee, and Yong Jeong Huh, "A Study on Shape Warpage Defect Detecion Model of Scaffold Using Deep Learning Based CNN," Journal of the Semiconductor & Display Technology, Vol. 20, No. 1. March 2021.