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- W3017592144 abstract "To date, various optimization algorithms have been employed to design and improve the performance of nanophotonic structures. Here, we propose to utilize a machine-learning algorithm viz. binary-Additive Reinforcement Learning Algorithm (b-ARLA) coupled with finite-difference time-domain (FDTD) simulations to design ultra-compact and efficient on-chip integrated nanophotonic 50:50 beam splitters (T-junctions). Here we present the design of two T-junction splitters each with a footprint of only 1.2 $mu$m x 1.2 $mu$m. To the best of our knowledge, these designs are amongst the smallest ever reported till date across either simulations or experiments. The simulated net power transmission efficiency for the first T-junction design is ~ 82% and the second design is ~ 80% $at 4lambda = 1.55 mu$m. We envision that the design methodology, as reported herein, would be useful in general for designing any efficient integrated-photonic device for optical communications systems." @default.
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- W3017592144 date "2020-09-01" @default.
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- W3017592144 title "Machine learning enables design of on-chip integrated silicon T-junctions with footprint of 1.2 <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=d1e1139 altimg=si6.svg><mml:mi mathvariant=normal>μ</mml:mi></mml:math>m<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=d1e1144 altimg=si7.svg><mml:mrow><mml:mo>×</mml:mo><mml:mspace width=1em class=nbsp /><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>2</mml:mn><mml:mspace width=1em…" @default.
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- W3017592144 doi "https://doi.org/10.1016/j.nancom.2020.100312" @default.
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