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arXiv 2011.11754physics.opticscs.LGphysics.app-ph
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Machine Learning enables Ultra-Compact Integrated Photonics through Silicon-Nanopattern Digital Metamaterials

University of Utah(犹他大学);School of Computing, University of Utah(犹他大学计算学院)

Sourangsu Banerji, Apratim Majumder, Alex Hamrick, Rajesh Menon, Berardi Sensale-Rodriguez

英文摘要:

In this work, we demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. Through digitizing the design domain into "binary pixels" these digital metamaterials are readily manufacturable as well. By showing a variety of devices (beamsplitters and waveguide bends), we showcase the generality of our approach. With an area footprint smaller than ${λ_0}^2$, 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."

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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