Deep learning for the design of nano-photonic structures

Itzik Malkiel, Michael Mrejen, Achiya Nagler, Uri Arieli, Lior Wolf, Haim Suchowski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Our visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has had a profound impact on the field of optics by enabling the manipulation of light-matter interaction with subwave-length structures [1, 2, 3]. However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages [4, 5, 6]. But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high non-linearity of the problem [7, 8]. Here, we harness the power of Deep Learning and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmons mediated cancer thermotherapy.

Original languageEnglish
Title of host publicationIEEE International Conference on Computational Photography, ICCP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages14
ISBN (Electronic)9781538625262
StatePublished - 29 May 2018
Event2018 IEEE International Conference on Computational Photography, ICCP 2018 - Pittsburgh, United States
Duration: 4 May 20186 May 2018

Publication series

NameIEEE International Conference on Computational Photography, ICCP 2018


Conference2018 IEEE International Conference on Computational Photography, ICCP 2018
Country/TerritoryUnited States


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