The emergent thin-film lithium niobate on insulator (TFLN or LNOI) photonic integrated circuits (PICs) offer significant advantages in various applications due to their unique properties. This paper briefly explores recent advancements in TFLN PIC developments and their broad applications, emphasizing transformative capabilities in telecommunications and beyond. We highlight CSEM's pioneering initiative in establishing the first open-access foundry for this technology, addressing challenges associated with limited access to manufacturing facilities and process design kits (PDK).
Photonic integrated circuits provide a scalable platform for photonics-based quantum technologies. However, integrating quantum emitters and electro-optic cavities within this platform remains an open challenge proving to be a major hurdle from implementing key functionalities for quantum photonics, such as single photon sources and nonlinearities. Here, we address this shortcoming with the hybrid integration of InAs/InP quantum dot emitters on foundry silicon photonics and the implementation of photonic crystal cavities in thin-film lithium niobate. Co-integrated on-chip electronics allow us to tune the emission properties of the quantum dots while enabling GHz-rate coherent modulation over photons trapped in the cavities, thus providing a new level of programmability over interactions between optical fields and atom-like systems in integrated circuits. Our results open the door to a new generation of quantum information processors that can be manufactured in leading semiconductor foundries.
Thin Film Lithium Niobate (TFLN) photonic integrated circuits offer several improvements over other platforms in terms of material loss, energy efficiency, and operational bandwidth. We review our recent demonstration of quadrature phase shift keying in an ultrasmall TFLN photonic crystal-based IQ modulator. Our modulator features a footprint of 40 × 200 μm2 along with quality factors approaching 105 providing it with a Vπ = 1.16 V [H. Larocque et al. CLEO 2023, paper STh1R.3; H. Larocque et al. arXiv:2312.16746]. We discuss an extension to and optimization of quadrature amplitude modulation encoding schemes tailored to the device’s voltage response, including the use of a deep neural network for streamlining bit error rate minimization.
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