Abstract
In this paper we report the experimental realization of a reconfigurable reflective arrayed waveguide grating on silicon nitride technology, using optimization algorithms borrowed from machine learning applications. A dozen of band-shape responses, as well as a spectral resolution change, are demonstrated in the optical telecom C-band, alongside a proof of operation of the same device in the O-band. In the context of programmable and reconfigurable integrated photonics, this building block supports multi-wavelength/band spectral shaping of optical signals that can serve to multiple applications.
Highlights
Integrated photonics is on the rise as a complementary technology to micro-electronics
In this paper we experimentally demonstrate for the first time to our knowledge, a reflective reconfigurable arrayed waveguide gratings (AWGs) (RRAWG) in silicon nitride technology, using tailored optimization algorithms borroed from machine learning applications
Phase trimming techniques have been applied to telecom AWGs, that set a static response to the device [36], but the active tuning capability of the tunable mirrors ideally can in first place alleviate up to some extent the fabrications deviations, and in second place provide the reconfiguration of the spectral response
Summary
Integrated photonics is on the rise as a complementary technology to micro-electronics. Fontaine and co-workers reported a reconfigurable band-pass AWG in silica [23], with amplitude and phase tuning within the arms, demonstrating up to six different band-shapes. The number of previous works on tunable AWGs is humongous, but restricting the search to high-index contrast silicon photonics technology, most address phase tuning to correct phase errors and improve the passband shape and out-of-band floor [24,25]. Other [26] report a minor re-shaping of the pass-band when tuning the center wavelength of the channels, through the phase of the AWG arms. In this paper we experimentally demonstrate for the first time to our knowledge, a reflective reconfigurable AWG (RRAWG) in silicon nitride technology, using tailored optimization algorithms borroed from machine learning applications.
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