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High-Q asymmetrically cladded silicon nitride 1D photonic crystals cavities and hybrid external cavity lasers for sensing in air and liquids.

ABSTRACT: In this paper we show a novel design of high Q-factor silicon nitride (SiN) 1D photonic crystal (PhC) cavities side-coupled to curved waveguides, operating with both silica and air cladding. The engineering of the etched 1D PhC cavity sidewalls angle allows for high Q-factors over a wide range of upper cladding compositions, and the achievement of the highest calculated Q-factor for non-suspended asymmetric SiN PhC structures. We show the employment of these type of SiN PhC cavities in hybrid external cavity laser (HECL) configuration, with mode-hop free single mode laser operation over a broad range of injected currents (from 25 mA to 65 mA), milliwatts of power output (up to 9 mW) and side-mode suppression ratios in the range of 40 dB. We demonstrate the operation of these devices as compact and energy efficient optical sensors that respond to refractive index changes in the surrounding medium the measurement of sodium chloride (from 0% to 25%) and sucrose (from 0% to 25%) in aqueous solution. In HECL configuration, the RI sensor exhibits a 2 orders of magnitude improvement in detection limit compared to the passive microcavity. We also discuss the possibility for applying these devices as novel transducers for refractive index changes that are induced by analyte specific absorption of infrared radiation by the target analytes present in gas or liquid phase.

SUBMITTER: Iadanza S

PROVIDER: S-EPMC9412843 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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High-Q asymmetrically cladded silicon nitride 1D photonic crystals cavities and hybrid external cavity lasers for sensing in air and liquids.

Iadanza Simone S Mendoza-Castro Jesus Hernan JH Oliveira Taynara T Butler Sharon M SM Tedesco Alessio A Giannino Giuseppe G Lendl Bernhard B Grande Marco M O'Faolain Liam L

Nanophotonics (Berlin, Germany) 20220810 18

In this paper we show a novel design of high Q-factor silicon nitride (SiN) 1D photonic crystal (PhC) cavities side-coupled to curved waveguides, operating with both silica and air cladding. The engineering of the etched 1D PhC cavity sidewalls angle allows for high Q-factors over a wide range of upper cladding compositions, and the achievement of the highest calculated Q-factor for non-suspended asymmetric SiN PhC structures. We show the employment of these type of SiN PhC cavities in hybrid ex ...[more]

PMID: 36147699

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Project description:Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-optimal. Machine learning has emerged as a powerful tool to automate this inverse design process. However, supervised or semi-supervised deep learning is unsuitable for this task due to: (1) a severe shortage of available training data due to the high computational complexity of physics-based simulations along with a lack of open-source datasets and/or the need for a pre-trained neural network model; (2) the issue of one-to-many mapping or non-unique solutions; and (3) the inability to perform optimization of the photonic structure beyond inverse designing. Reinforcement Learning (RL) has the potential to overcome the above three challenges. Here, we propose Learning to Design Optical-Resonators (L2DO) to leverage RL that learns to autonomously inverse design nanophotonic laser cavities without any prior knowledge while retrieving unique design solutions. L2DO incorporates two different algorithms - Deep Q-learning and Proximal Policy Optimization. We evaluate L2DO on two laser cavities: a long photonic crystal (PC) nanobeam and a PC nanobeam with an L3 cavity, both popular structures for semiconductor lasers. Trained for less than 152 hours on limited hardware resources, L2DO has improved state-of-the-art results in the literature by over 2 orders of magnitude and obtained 10 times better performance than a human expert working the same task for over a month. L2DO first learned to meet the required maxima of Q-factors (>50 million) and then proceeded to optimize some additional good-to-have features (e.g., resonance frequency, modal volume). Compared with iterative human designs and inverse design via supervised learning, L2DO can achieve over two orders of magnitude higher sample-efficiency without suffering from the three issues above. This work confirms the potential of deep RL algorithms to surpass human designs and marks a solid step towards a fully automated AI framework for photonics inverse design.

Dataset Information

High-Q asymmetrically cladded silicon nitride 1D photonic crystals cavities and hybrid external cavity lasers for sensing in air and liquids.

Publications

High-Q asymmetrically cladded silicon nitride 1D photonic crystals cavities and hybrid external cavity lasers for sensing in air and liquids.

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