Deep Learning-designed Diffractive Neural Networks

被引：0

作者：

Lin, Xing ^{[1
,2
,3
]}

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

Yardimci, Nezih T. ^{[1
,3
]}

Veli, Muhammed ^{[1
,2
,3
]}

Luo, Yi ^{[1
,2
,3
]}

Jarrahi, Mona ^{[1
]}

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

机构：

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

[2] Univ Calif Los Angeles, David Geffen Sch Med, Bioengn Dept, Los Angeles, CA 90095 USA

[3] Univ Calif Los Angeles, David Geffen Sch Med, 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：

2019 CONFERENCE ON LASERS AND ELECTRO-OPTICS (CLEO) | 2019年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We report deep learning-based design of diffractive neural networks. Following its fabrication, a diffractive neural network can all-optically perform user-defined tasks through diffraction. We demonstrate the applications of this framework for classification and imaging tasks. (C) 2019 The Author(s)

引用

页数：2

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