Flexografically printed optical waveguides for complex low-cost optical networks

Pflieger, Keno; Overmeyer, Ludger; Olsen, Ejvind

doi:10.1117/12.2606364

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…In previous research, printed optical waveguides showcased the ability to be highly cost-efficient due to their purely additive characteristic. Due to their polymer base substrate and multimode scale cross-section dimension, printed waveguides qualify for optical coupling challenges and adaptivity to PCB requirements [12][13]. Regarding printing processes, multiple techniques were investigated and evaluated according to their capability to directly print polymer waveguides on PMMA-Substrate.…”

Section: Introductionmentioning

confidence: 99%

Rotative printing processes for polymer waveguide manufacturing

Evertz,

Fütterer,

Fritze

et al. 2023

Optifab 2023

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Section: Introductionmentioning

confidence: 99%

Rotative printing processes for polymer waveguide manufacturing

Evertz,

Fütterer,

Fritze

et al. 2023

Optifab 2023

Self Cite

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Mosquito-dispensed waveguides in cavities on 3D-MIDs

Fütterer,

Zander,

Olsen

et al. 2023

2023 15th International Congress Mechatronic Integration Discourse (MID)

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Deep learning approach to predict optical attenuation in additively manufactured planar waveguides

Pflieger,

Evertz,

Overmeyer

2023

Appl. Opt.

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The booming demand for efficient, scalable optical networks has intensified the exploration of innovative strategies that seamlessly connect large-scale fiber networks with miniaturized photonic components. Within this context, our research introduces a neural network, specifically a convolutional neural network (CNN), as a trailblazing method for approximating the nonlinear attenuation function of centimeter-scale multimode waveguides. Informed by a ray tracing model that simulated many flexographically printed waveguide configurations, we cultivated a comprehensive dataset that laid the groundwork for rigorous CNN training. This model demonstrates remarkable adeptness in estimating optical losses due to waveguide curvature, achieving an attenuation standard deviation of 1.5 dB for test data over an attenuation range of 50 dB. Notably, the CNN model’s evaluation speed, at 517 µs per waveguide, starkly contrasts the used ray tracing model that demands 5–10 min for a similar task. This substantial increase in computational efficiency accentuates the model’s paramount significance, especially in scenarios mandating swift waveguide assessments, such as optical network optimization. In a subsequent study, we test the trained model on actual measurements of fabricated waveguides and its optical model. All approaches show excellent agreement in assessing the waveguide’s attenuation within measurement accuracy. Our endeavors elucidate the transformative potential of machine learning in revolutionizing optical network design.

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Flexografically printed optical waveguides for complex low-cost optical networks

Cited by 3 publications

References 16 publications

Rotative printing processes for polymer waveguide manufacturing

Rotative printing processes for polymer waveguide manufacturing

Mosquito-dispensed waveguides in cavities on 3D-MIDs

Deep learning approach to predict optical attenuation in additively manufactured planar waveguides

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