Resolution enhancement in scanning electron microscopy using deep learning

被引：96

作者：

de Haan, Kevin ^{[1
,2
,3
]}

Ballard, Zachary S. ^{[1
,2
,3
]}

Rivenson, Yair ^{[1
,2
,3
]}

Wu, Yichen ^{[1
,2
,3
]}

Ozcan, Aydogan ^{[1
,2
,3
,4
]}

机构：

[1] Univ Calif Los Angeles, Elect & Comp Engn Dept, Los Angeles, CA 90095 USA

[2] Univ Calif Los Angeles, Bioengn Dept, Los Angeles, CA 90095 USA

[3] Univ Calif Los Angeles, Calif NanoSyst Inst CNSI, Los Angeles, CA 90095 USA

[4] Univ Calif Los Angeles, David Geffen Sch Med, Dept Surg, Los Angeles, CA 90095 USA

来源：

SCIENTIFIC REPORTS | 2019年 / 9卷 / 1期

关键词：

SILICON-WAFERS; DAMAGE;

D O I：

10.1038/s41598-019-48444-2

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples.

引用

页数：7

共 27 条

[21] Sparsity-Based Super Resolution for SEM Images [J].