Design of a Silicon Mach-Zehnder Modulator via Deep Learning and Evolutionary Algorithms Article Swipe
Romulo Aparecido de Paula
,
Ivan Aldaya
,
Tiago Sutili
,
Rafael Figueirêdo
,
Julián L. Pita
,
Yésica R. R. Bustamante
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2602338/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2602338/v1
As an essential block in optical communication systems, silicon (Si) Mach-Zehnder modulators (MZMs) are approaching the limits of possible performance for high-speed applications. However, due to a large number of design parameters and the complex simulation of these devices, achieving high-performance configuration employing conventional optimization methods result in prohibitively long times and use of resources. Here, we propose a design methodology based on artificial neural networks and heuristic optimization that significantly reduces the complexity of the optimization process. First, we implemented a deep neural network model to substitute the 3D electromagnetic simulation of a Si-based MZM, whereas subsequently, this model is used to estimate the figure of merit within the heuristic optimizer, which, in our case, is the differential evolution algorithm. By applying this method to CMOS-compatible MZMs, we find new optimized configurations in terms of electro-optical bandwidth, insertion loss, and half-wave voltage. In particular, we achieve configurations of MZMs with a 40 GHz bandwidth and a driving voltage of 6.25 V, or, alternatively, 47.5 GHz with a driving voltage of 8 V. Furthermore, the faster simulation allowed optimizing MZM subject to different constraints, which permits us to explore the possible performance boundary of this type of MZMs.
Related Topics
Concepts
Bandwidth (computing)
Computer science
Artificial neural network
Voltage
Heuristic
Electronic engineering
Differential evolution
CMOS
Evolutionary algorithm
Block (permutation group theory)
Algorithm
Computer engineering
Artificial intelligence
Telecommunications
Mathematics
Engineering
Electrical engineering
Geometry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2602338/v1
- https://www.researchsquare.com/article/rs-2602338/latest.pdf
- OA Status
- green
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323664640
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4323664640Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2602338/v1Digital Object Identifier
- Title
-
Design of a Silicon Mach-Zehnder Modulator via Deep Learning and Evolutionary AlgorithmsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-09Full publication date if available
- Authors
-
Romulo Aparecido de Paula, Ivan Aldaya, Tiago Sutili, Rafael Figueirêdo, Julián L. Pita, Yésica R. R. BustamanteList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2602338/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-2602338/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-2602338/latest.pdfDirect OA link when available
- Concepts
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Bandwidth (computing), Computer science, Artificial neural network, Voltage, Heuristic, Electronic engineering, Differential evolution, CMOS, Evolutionary algorithm, Block (permutation group theory), Algorithm, Computer engineering, Artificial intelligence, Telecommunications, Mathematics, Engineering, Electrical engineering, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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