Inverse Design of Nanophotonic Devices using Deep Neural Networks

被引：0

作者：

Kojima, Keisuke ^{[1
,2
]}

Tang, Yingheng ^{[1
,3
]}

Koike-Akino, Toshiaki ^{[1
]}

Wang, Ye ^{[1
]}

Jha, Devesh ^{[1
]}

Parsons, Kieran ^{[1
]}

Tahersima, Mohammad H. ^{[1
]}

Sang, Fengqiao ^{[2
]}

Klamkin, Jonathan ^{[2
]}

Qi, Minghao ^{[3
]}

机构：

[1] Mitsubishi Elect Res Labs MERL, 201 Broadway, Cambridge, MA 02139 USA

[2] Univ Calif Santa Barbara, Elect & Comp Engn Dept, Santa Barbara, CA 93106 USA

[3] Purdue Univ, Elect & Comp Engn Dept, W Lafayette, IN 47907 USA

来源：

2020 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE (ACP) AND INTERNATIONAL CONFERENCE ON INFORMATION PHOTONICS AND OPTICAL COMMUNICATIONS (IPOC) | 2020年

关键词：

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We present three different approaches to apply deep learning to inverse design for nanophotonic devices. The forward and inverse regression models use device parameters as inputs and device responses as outputs, and vice versa. The generative model to create a series of improved designs. We demonstrate them to design nanophotonic power splitters with multiple splitting ratios. (C) 2020 The Author(s)

引用

页数：3

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