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Title: Ultra-compact integrated photonic devices enabled by machine learning and digital metamaterials
We demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. By digitizing the design domain into “binary pixels,” these digital metamaterials are readily manufacturable using traditional semiconductor foundry processes. By showing various devices (beam-splitters and waveguide bends), we showcase our approach's generality. With an area footprint smaller than λ02, our designs are amongst the smallest reported to-date. Our method combines machine learning with digital metamaterials to enable ultra-compact, manufacturable devices, which could power a new “Photonics Moore's Law.”  more » « less
Award ID(s):
1936729 1828480
PAR ID:
10212398
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
OSA Continuum
Volume:
4
Issue:
2
ISSN:
2578-7519
Format(s):
Medium: X Size: Article No. 602
Size(s):
Article No. 602
Sponsoring Org:
National Science Foundation
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