Photo-Elastic Enhanced Optomechanic One Dimensional Phoxonic Fishbone Nanobeam

Hsiao, Fu‐Li; Tsai, Ying-Pin; Chang, Wei-Shan; Chiu, Chien-Chang; Lin, Bor‐Shyh; Chiang, Chi-Tsung

doi:10.3390/cryst12070890

Cited by 3 publications

(2 citation statements)

References 35 publications

(42 reference statements)

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“…To simplify the fabrication process, these pillars are usually arranged on the side walls of the nanobeam and remain with the main waveguide in the same plane, producing a fishbone-like PtC structure [36][37][38]. This kind of PtC has the advantage of an acousto-optic structure, which is a result of its fishbone-like pillars [39][40][41], and due to this pillar-type structure, it can generate phononic band gaps much more easily than a hole-type structure could, producing high-quality acoustic resonators that can be combined with the photonic resonators perfectly.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Geometric Parameters for Training an Artificial Neural Network to Predict the Band Structure of 1-D Fishbone Photonic Crystal

Hsiao,

Chen,

Chang

et al. 2024

Electronics

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With the rising demand for the transmission of large amounts of information over long distances, the development of integrated light circuits is the key to improving this technology, and silicon photonics have been developed with low absorption in the near-infrared range and with sophisticated fabrication techniques. To build devices that work in different functionalities, photonic crystals are one of the most used structures due to their ability to manipulate light. The investigation of photonic crystals requires the calculation of photonic band structures and is usually time-consuming work. To reduce the time spent on calculations, a trained ANN is introduced in this study to directly predict the band structures using only a minimal amount of pre-calculated band structure data. A well-used 1-D fishbone-like photonic crystal in the form of a nanobeam is used as the training target, and the influence of adjusting the geometric parameters is discussed, especially the lattice constant and the thickness of the nanobeam. To train the ANN with very few band structures, each of the mode points in the band structure is considered as a single datapoint to increase the amount of training data. The datasets are composed of various raw band structure data. The optimized ANN is introduced at the end of this manuscript.

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Section: Introductionmentioning

confidence: 99%

The Influence of Geometric Parameters for Training an Artificial Neural Network to Predict the Band Structure of 1-D Fishbone Photonic Crystal

Hsiao,

Chen,

Chang

et al. 2024

Electronics

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“…Most phoxonic nanobeams or optomechanic nanobeams are designed to simultaneously guide the photons and phonons and trap them in a cavity. The nanobeam will include a cavity region sandwiched by two mirror regions, and the acoustic band gap and the optic band gap designed in the mirror region can trap the corresponding mode in the cavity region and induce acousto-optic coupling [24,[31][32][33][34]. Such modes in nanobeam structures can be measured by another fiber near the nanobeam.…”

Section: Introductionmentioning

confidence: 99%

High-Q Slow Sound Mode in a Phononic Fishbone Nanobeam Using an Acoustic Potential Well Cavity

2023

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Phononic crystals and phononic metamaterials are popular structures for manipulating acoustic waves with artificially arranged units that have different elastic constants. These structures are also used in acousto-optic coupling and optomechanical structures. In such research, a 1-D nanobeam containing a cavity region sandwiched by two mirror regions is one of the most common designs. However, searching bandgaps for suitable operation modes and the need for the mirror region are limitations in the device design. Therefore, we introduce the slow sound mode as the operating acoustic mode and use an acoustic potential well to further trap the phonons in the cavity. Three types of structures are introduced to investigate the effect of the potential well. The products of the mode frequencies and the quality factors of the modes are used to demonstrate the performance of the structures. The displacement field and the strain field show the concentrated slow sound modes of the potential wells and produce high quality factors.

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Slow sound mode prediction and band structure calculation in 1D phononic crystal nanobeams using an artificial neural network

Hsiao,

Yang,

Lin

et al. 2024

Science Progress

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Phononic crystals, which are artificial crystals formed by the periodic arrangement of materials with different elastic coefficients in space, can display modulated sound waves propagating within them. Similar to the natural crystals used in semiconductor research with electronic bandgaps, phononic crystals exhibit the characteristics of phononic bandgaps. A gap design can be utilized to create various resonant cavities, confining specific resonance modes within the defects of the structure. In studies on phononic crystals, phononic band structure diagrams are often used to investigate the variations in phononic bandgaps and elastic resonance modes. As the phononic band frequencies vary nonlinearly with the structural parameters, numerous calculations are required to analyze the gap or mode frequency shifts in phononic band structure diagrams. However, traditional calculation methods are time-consuming. Therefore, this study proposes the use of neural networks to replace the time-consuming calculation processes of traditional methods. Numerous band structure diagrams are initially obtained through the finite-element method and serve as the raw dataset, and a certain proportion of the data is randomly extracted from the dataset for neural network training. By treating each mode point in the band structure diagram as an independent data point, the training dataset for neural networks can be expanded from a small number to a large number of band structure diagrams. This study also introduces another network that effectively improves mode prediction accuracy by training neural networks to focus on specific modes. The proposed method effectively reduces the cost of repetitive calculations.

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Photo-Elastic Enhanced Optomechanic One Dimensional Phoxonic Fishbone Nanobeam

Cited by 3 publications

References 35 publications

The Influence of Geometric Parameters for Training an Artificial Neural Network to Predict the Band Structure of 1-D Fishbone Photonic Crystal

The Influence of Geometric Parameters for Training an Artificial Neural Network to Predict the Band Structure of 1-D Fishbone Photonic Crystal

High-Q Slow Sound Mode in a Phononic Fishbone Nanobeam Using an Acoustic Potential Well Cavity

Slow sound mode prediction and band structure calculation in 1D phononic crystal nanobeams using an artificial neural network

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