Spectra2pix: Generating Nanostructure Images from Spectra Article Swipe
Itzik Malkiel
,
Michael Mrejen
,
Lior Wolf
,
Haim Suchowski
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.11525
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1911.11525
The design of the nanostructures that are used in the field of nano-photonics has remained complex, very often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies suggesting to apply Machine Learning techniques for the design of nanostructures. Most of these studies engage Deep Learning techniques, which entails training a Deep Neural Network (DNN) to approximate the highly non-linear function of the underlying physical process between spectra and nanostructures. At the end of the training, the DNN allows an on-demand design of nanostructures, i.e. the model can infer nanostructure geometries for desired spectra. In this work, we introduce spectra2pix, which is a model DNN trained to generate 2D images of the designed nanostructures. Our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. We show, for the first time, a successful generalization ability by designing a completely unseen sub-family of geometries. This generalization capability highlights the importance of our model architecture, and allows higher applicability for real-world design problems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1911.11525
- https://arxiv.org/pdf/1911.11525
- OA Status
- green
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2990294447
All OpenAlex metadata
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https://openalex.org/W2990294447Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1911.11525Digital Object Identifier
- Title
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Spectra2pix: Generating Nanostructure Images from SpectraWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-11-26Full publication date if available
- Authors
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Itzik Malkiel, Michael Mrejen, Lior Wolf, Haim SuchowskiList of authors in order
- Landing page
-
https://arxiv.org/abs/1911.11525Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1911.11525Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1911.11525Direct OA link when available
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Nanostructure, Artificial neural network, Intuition, Limiting, Computer science, Artificial intelligence, Deep learning, Architecture, Machine learning, Nanotechnology, Materials science, Engineering, Mechanical engineering, Cognitive science, Visual arts, Art, PsychologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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12Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.designing | 166 |
| abstract_inverted_index.expertise | 23 |
| abstract_inverted_index.geometry. | 154 |
| abstract_inverted_index.introduce | 116 |
| abstract_inverted_index.intuition | 21 |
| abstract_inverted_index.on-demand | 98 |
| abstract_inverted_index.problems. | 190 |
| abstract_inverted_index.training, | 93 |
| abstract_inverted_index.capability | 175 |
| abstract_inverted_index.completely | 168 |
| abstract_inverted_index.geometries | 108 |
| abstract_inverted_index.highlights | 176 |
| abstract_inverted_index.importance | 178 |
| abstract_inverted_index.increasing | 42 |
| abstract_inverted_index.non-linear | 77 |
| abstract_inverted_index.real-world | 188 |
| abstract_inverted_index.sub-family | 170 |
| abstract_inverted_index.successful | 162 |
| abstract_inverted_index.suggesting | 46 |
| abstract_inverted_index.techniques | 51 |
| abstract_inverted_index.ultimately | 27 |
| abstract_inverted_index.underlying | 81 |
| abstract_inverted_index.approximate | 74 |
| abstract_inverted_index.geometries. | 172 |
| abstract_inverted_index.penetration | 32 |
| abstract_inverted_index.techniques, | 64 |
| abstract_inverted_index.architecture | 134 |
| abstract_inverted_index.spectra2pix, | 117 |
| abstract_inverted_index.applicability | 186 |
| abstract_inverted_index.architecture, | 182 |
| abstract_inverted_index.nanostructure | 107, 143 |
| abstract_inverted_index.generalization | 163, 174 |
| abstract_inverted_index.groundbreaking | 35 |
| abstract_inverted_index.nano-photonics | 12 |
| abstract_inverted_index.nanostructures | 4 |
| abstract_inverted_index.nanostructures, | 101 |
| abstract_inverted_index.nanostructures. | 56, 87, 131 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |