Abstract

In semiconductor manufacturing, critical dimensions indicate the features of patterns formed by the semiconductor process. The purpose of measuring critical dimensions is to confirm whether patterns are made as intended. The deposition process for an organic light emitting diode (OLED) forms a luminous organic layer on the thin-film transistor electrode. The position of this organic layer greatly affects the luminescent performance of an OLED. Thus, a system for measuring the position of the organic layer from outside of the vacuum chamber in real-time is desired for monitoring the deposition process. Typically, imaging from large stand-off distances results in low spatial resolution because of diffraction blur, and it is difficult to attain an adequate industrial-level measurement. The proposed method offers a new superresolution single-image using a conversion formula between two different optical systems obtained by a deep learning technique. This formula converts an image measured at long distance and with low-resolution optics into one image as if it were measured with high-resolution optics. The performance of this method is evaluated with various samples in terms of spatial resolution and measurement performance.

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  21. Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. OzcanDeep learning microscopyOptica2017414371443
  22. J. Lee, Y. Kim, S. Kim, I. Lee, and H. PahkReal-time application of critical dimension measurement of TFT-LCD pattern using a newly proposed 2D image-processing algorithmOpt. Lasers Eng.200846558569
  23. R. M. HaralickDigital step edge from zero crossing of second directional derivativesIEEE Trans. Pattern Anal. Mach. Intell.198465868
  24. S. McHughDigital photography tutorials2005

Other (24)

B. Geffroy, P. L. Roy, and C. PratOrganic light-emitting diode (OLED) technology: Materials, devices and display technologiesPolym. Int.200655572582

K. Venkataraman, D. Lelescu, J. Duparré, A. McMahon, G. Molina, P. Chatterjee, R. Mullis, and S. NayarPicam: An ultra-thin high performance monolithic camera arrayACM Trans. Graph.201332166

G. Carles, J. Downing, and A. R. HarveySuper-resolution imaging using a camera arrayOpt. Lett.20143918891892

J. Holloway, Y. Wu, M. K. Sharma, O. Cossairt, and A. VeeraraghavanSAVI: Synthetic apertures for long-range, subdiffraction-limited visible imaging using fourier ptychographySci. Adv.20173e16025641602564

G. Zheng, R. Horstmeyer, and C. YangWide-field, high-resolution fourier ptychographic microscopyNat. Photon.20137739745

S. Dong, Z. Bian, and R. ShiradkarSparsely sampled fourier ptychographyOpt. Express20142254555464

K. Guo, S. Dong, P. Nanda, and G. ZhengOptimization of sampling pattern and the design of fourier ptychographic illuminatorOpt. Express20152361716180

S. Dong, R. Horstmeyer, R. Shiradkar, K. Guo, X. Ou, Z. Bian, H. Xin, and G. ZhengAperture-scanning fourier ptychography for 3d refocusing and super-resolutionOpt. Express2014221358613599

N. T. Doan, J. H. Moon, T. W. Kim, and H. J. PahkA fast image enhancement technique using a new scanning path for critical dimension measurement of glass panelsInt. J. Precis. Eng. Man20121321092114

C. Dong, C. C. Loy, K. He, and X. TangLearning a deep convolutional network for image super-resolutionEuropean Conference on Computer Vision (ECCV)6-12 Sept. 2014Chem184199

C. Dong, C. C. Loy, K. He, and X. TangAccelerating the superresolution convolutional neural networkEuropean Conference on Computer Vision (ECCV)11-14, Oct. 2016Amsterdam391407

J. Kim, J. K. Lee, and K. M. LeeAccurate image super-resolution using very deep convolutional networksProc. IEEE Conference on Computer Vision and Pattern RecognitionIEEE201616461654

W. Bae and J. YooBeyond deep residual learning for image restoration: Persistent homology-guided manifold simplificationhttps://arxiv.org/abs/1611.06345

Y. S. Han, J. Yoo, and J. C. YeDeep residual learning for compressed sensing CT reconstruction via persistent homology analysishttps://arxiv.org/abs/1611.06391

B. Lim, S. H. Son, H. W. Kim, S. J. Nah, and K. M. LeeEnhanced deep residual networks for single image super-resolutionProc. IEEE Conference on Computer Vision and Pattern Recognition WorkshopIEEE2017136144

L. Xu, J. Ren, C. Liu, and J. JiaDeep convolutional neural network for image deconvolutionProc. IEEE Conference on Advances in Neural Information Processing SystemsIEEE201417901798

J. Xie, L. Xu, and E. CheImage denoising and inpainting with deep neural networksProc. IEEE Conference on Advances in Neural Information Processing SystemsIEEE2012341349

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. ZhangBeyond a gaussian denoiser: Residual learning of deep CNN for image denoisingIEEE Trans. Image Process20172631423155

R. Timofte, E. Agustsson, L. V. Gool, M.-H. Yang, L. Zhang, B. Lim, S. Son, H. Kim, S. Nah, K. M. Lee, X. Wang, Y. Tian, K. Yu, Y. Zhang, S. Wu, C. Dong, L. Lin, Y. Qiao, C. C. Loy, W. Bae, J. Yoo, Y. Han, J. C. Ye, J.-S. Choi, M. Kim, Y. Fan, J. Yu, W. Han, D. Liu, H. Yu, Z. Wang, H. Shi, X. Wang, T. S. Huang, Y. Chen, K. Zhang, W. Zuo, Z. Tang, L. Luo, S. Li, M. Fu, L. Cao, W. Heng, G. Bui, T. Le, Y. Duan, D. Tao, R. Wang, X. Lin, J. Pang, J. Xu, Y. Zhao, X. Xu, J. Pan, D. Sun, Y. Zhang, X. Song, Y. Dai, X. Qin, X.-P. Huynh, T. Guo, H. S. Mousavi, T. H. Vu, V. Monga, C. Cruz, K. Egiazarian, V. Katkovnik, R. Mehta, A. K. Jain, A. Agarwalla, C. V. S. Praveen, R. Zhou, H. Wen, C. Zhu, Z. Xia, Z. Wang, and Q. GuoNTIRE 2017 challenge on single image super-resolution: Method and resultsProc. IEEE Conference on Computer Vision and Pattern Recognition WorkshopsIEEE201711101121

D. G. LoweDistinctive image features from scale-invariant keypointsInt. J. Comput. Vis.20046091110

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. OzcanDeep learning microscopyOptica2017414371443

J. Lee, Y. Kim, S. Kim, I. Lee, and H. PahkReal-time application of critical dimension measurement of TFT-LCD pattern using a newly proposed 2D image-processing algorithmOpt. Lasers Eng.200846558569

R. M. HaralickDigital step edge from zero crossing of second directional derivativesIEEE Trans. Pattern Anal. Mach. Intell.198465868

S. McHughDigital photography tutorials2005

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