The Design of a Low-Loss, Fast-Response, Metal Thermo-Optic Phase Shifter Based on Coupled-Mode Theory
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In this article , the authors proposed a method to place high-loss materials close to the optical waveguide while maintaining the low loss of the optical device, which ensures the low insertion loss (~0.78 dB) of the phase shifter.Abstract:
A thermo-optic phase shifter is of great importance in silicon photonics. However, it is difficult to simultaneously achieve a good performance in insertion loss and in response speed by using traditional thermal tuning through a metallic heater. In this paper, based on coupled-mode theory, we propose a method to place high-loss materials close to the optical waveguide while maintaining the low loss of the optical device, which ensures the low insertion loss (~0.78 dB) of the phase shifter. Additionally, thanks to the very short distance between the rib waveguide and the chromium (Cr) heater, the phase shifter exhibits a high response speed (1.15 μs in rise time and 2.18 μs in decay time) with a measured bandwidth (BW) of 186 kHz. Moreover, we further optimize the structure of phase shifters, leading to the reduction of π-shift power consumption from 25.1 mW to 13.6 mW. Our proposed phase shifters have great potential in large-scale silicon photonic integrated circuits.read more
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An Efficient, Fast‐Responding, Low‐Loss Thermo‐Optic Phase Shifter Based on a Hydrogen‐Doped Indium Oxide Microheater
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TL;DR: In this paper , an energy-efficient, fast-responding, low-loss TO phase shifter is demonstrated by introducing hydrogen-doped indium oxide (IHO) films as the microheater, and the optimized electron concentration with enhanced mobility endows the IHO with high conductivity as well as high near-infrared (NIR) transparency, which allow it to directly contact the silicon waveguide without any insulating layer for efficient tuning and fast response.
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TL;DR: In this article , a new machine learning assisted two-dimensional beam formation and steering technique is presented, which calibrates phase errors via a convolutional neural network architecture to mitigate phase errors emanating from fabrication imperfections, thermal crosstalk and wavelength tuning effects.
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