Efficient telecom-to-visible spectral translation through ultra-low power nonlinear nanophotonics

Published

June 24, 2019

Author(s)

Xiyuan Lu, Gregory Moille, Qing Li, Daron Westly, Anshuman Singh, Ashutosh Rao, Su P. Yu, Travis Briles, Scott Papp, Kartik Srinivasan

Abstract

The ability to spectrally translate lightwave signals in a compact, low-power platform is at the heart of the promise of nonlinear nanophotonic technologies. For example, a device to connect the telecommunications band with visible and short near-infrared wavelengths can enable a connection between high-performance chip-integrated lasers based on scalable nanofabrication technology with atomic systems used for time and frequency metrology. While second-order nonlinear (chi^(2)) systems are a natural approach for bridging such large spectral gaps, here we show that third-order nonlinear (chi^(3)) systems, despite their much weaker nonlinear coefficients, can realize spectral translation with unprecedented performance. By combining resonant enhancement with nanophotonic mode engineering in a silicon nitride microring resonator, we demonstrate efficient spectral translation of a continuous-wave telecom signal at 1573 nm to a visible wavelength at 670 nm through cavity-enhanced four-wave mixing. We achieve translation over a spectral range over 250 THz and a translation efficiency of (30.1 +/- 2.8) % using only (329 +/- 13) μW pump power. The translation efficiency projects to (274 +/- 28) % at 1 mW and is more than an order of magnitude larger than what has been achieved in current nanophotonic devices.

Citation

Nature Photonics

Volume

13

Issue

9

Pub Type

Journals

Download Paper

https://doi.org/10.1038/s41566-019-0464-9

Local Download

Citation

Lu, X. , Moille, G. , Li, Q. , Westly, D. , Singh, A. , Rao, A. , Yu, S. , Briles, T. , Papp, S. and Srinivasan, K. (2019), Efficient telecom-to-visible spectral translation through ultra-low power nonlinear nanophotonics, Nature Photonics, [online], https://doi.org/10.1038/s41566-019-0464-9, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927352 (Accessed December 11, 2024)