Deep learning-enabled resolution enhancement of scanning electron microscopy images

Kevin de Haan; Zachary S. Ballard; Yair Rivenson; Yichen Wu; Aydogan Ozcan

doi:10.1117/12.2567504

20 August 2020 Deep learning-enabled resolution enhancement of scanning electron microscopy images

Kevin de Haan, Zachary S. Ballard, Yair Rivenson, Yichen Wu, Aydogan Ozcan

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690P (2020) https://doi.org/10.1117/12.2567504
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

We present a deep learning-based framework to perform single image super-resolution of SEM images. We experimentally demonstrated that this network can enhance the resolution of SEM images by two-fold, allowing for a reduction of the scanning time and electron dosage by four-fold without any significant loss of image quality. Using blindly tested regions of a gold-on-carbon resolution test target, we quantitatively and qualitatively confirmed the image enhancement achieved by the trained network. We believe that this technique has the potential to improve the SEM imaging process, particularly in cases where imaging throughput and minimizing beam damage are of utmost importance.

Conference Presentation

Citation Download Citation

Kevin de Haan, Zachary S. Ballard, Yair Rivenson, Yichen Wu, and Aydogan Ozcan "Deep learning-enabled resolution enhancement of scanning electron microscopy images", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690P (20 August 2020); https://doi.org/10.1117/12.2567504

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