Mr. Shang-Hung Liu Profile

Shang-Hung Liu

at Enablence Technologies Inc

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Proceedings Article | 17 March 2023 Presentation + Paper

Enabling advanced photonic architectures using state-of-the-art silica-on-silicon planar lightwave circuit platform

S. Bidnyk, K. Yadav, S.-H. Liu, A. Balakrishnan

Proceedings Volume 12424, 1242406 (2023) https://doi.org/10.1117/12.2647016

KEYWORDS: Photonic integrated circuits, Optical coherence tomography, Polarization, LIDAR, Telecommunications, Optics manufacturing, Machine learning, Polarization control, Waveguides, Silica

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We present advanced photonic architectures based on a state-of-the-art silica-on-silicon planar lightwave circuit (PLC) platform. The industry-leading performance characteristics of the platform with respect to fiber coupling, propagation losses, and polarization control have led to its mass deployments in telecom and datacom applications. Recently, a broad range of architectures has emerged that take advantage of the unmatched phase control in silica-on-silicon PLCs for demanding applications. We review some exceptional capabilities of our PLC platform and discuss how ultra-low propagation loss of <0.009 dB/cm is achieved concurrently with polarization-insensitive operation with relatively high confinement that allows loss-free waveguide bends with a 1 mm radius of curvature. Combined with fiber-matched mode converters and temperature-stable operation (< 10 pm/°C), consistent performance is achieved across entire optical communication bands. End-to-end optimizations allow us to reach high performance in advanced optical building blocks such as cascaded lattice filters, polarization-beam splitters, arrayed waveguide gratings (AWGs), and coherent systems. The robustness and versatility of the platform are demonstrated by a survey of mature designs that encompass multiple classes of applications. We discuss multi-channel (de-)multiplexer designs that address the challenging requirements of today’s datacom and telecom deployments, the realization of 10+ meter long delay lines and K-clocks, the utilization of the platform in optical coherence tomography (OCT) systems, and PLC-based solutions for automotive manufacturers of LiDAR systems. Finally, we discuss how our design, manufacturing, and testing processes are controlled with machine learning, allowing in-situ monitoring of wafer fabrication, real-time process adjustments, and wafer-level predictions of device performance across a wide range of performance metrics.

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