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Nanophotonic particle simulation and inverse design using artificial neural networks
| dc.contributor.author | Cano-Renteria, Fidel | |
| dc.contributor.author | Tegmark, Max | |
| dc.contributor.author | Soljacic, Marin | |
| dc.contributor.author | Joannopoulos, John D. | |
| dc.contributor.author | Peurifoy, John | |
| dc.contributor.author | Shen, Yichen | |
| dc.contributor.author | Jing, Li | |
| dc.contributor.author | Yang, Yi | |
| dc.contributor.author | DeLacy, Brendan G. | |
| dc.date.accessioned | 2021-09-20T18:21:05Z | |
| dc.date.available | 2021-09-20T18:21:05Z | |
| dc.identifier.isbn | 9781510615373 | |
| dc.identifier.isbn | 9781510615380 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/132120 | |
| dc.description.abstract | © 2018 SPIE. We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used solve nanophotonic inverse design problems by using back-propogation - where the gradient is analytical, not numerical. | en_US |
| dc.publisher | SPIE-Intl Soc Optical Eng | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1117/12.2289195 | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.source | SPIE | en_US |
| dc.title | Nanophotonic particle simulation and inverse design using artificial neural networks | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Cano-Renteria, Fidel, Max Tegmark, Marin Soljacic, John D. Joannopoulos, John Peurifoy, Yichen Shen, Li Jing, Yi Yang, and Brendan G. DeLacy. “Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks.” Edited by Marek Osiński, Yasuhiko Arakawa, and Bernd Witzigmann. Physics and Simulation of Optoelectronic Devices XXVI (February 23, 2018). doi:10.1117/12.2289195. | en_US |
| dc.relation.journal | Physics and Simulation of Optoelectronic Devices XXVI | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2019-03-28T17:13:35Z | |
| dspace.orderedauthors | Cano-Renteria, Fidel; Tegmark, Max; Soljacic, Marin; Joannopoulos, John D.; Peurifoy, John; Shen, Yichen; Jing, Li; Yang, Yi; DeLacy, Brendan G. | en_US |
| dspace.embargo.terms | N | en_US |
| dspace.date.submission | 2019-04-04T12:16:48Z | |
| mit.license | PUBLISHER_POLICY | en_US |
| mit.metadata.status | Authority Work and Publication Information Needed |
