Racetrack Ring Resonator Integrated with Multimode Interferometer Structure Based on Low-Cost Silica–Titania Platform for Refractive Index Sensing Application

Butt, Muhammad A.; Shahbaz, Muhammad; Piramidowicz, Ryszard

doi:10.3390/photonics10090978

Cited by 28 publications

(8 citation statements)

References 50 publications

(65 reference statements)

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“…Their ability to confine light within the ring structure for prolonged periods enhances the interaction with analytes, leading to enhanced detection limits. Furthermore, the resonance characteristics of RTRRs can be fine-tuned by adjusting their geometry or utilizing different materials, offering a high degree of versatility in sensor design suitable for a wide range of applications spanning from biochemical sensing to environmental monitoring [12,13].…”

Section: Introductionmentioning

confidence: 99%

Racetrack Ring Resonator-Based on Hybrid Plasmonic Waveguide for Refractive Index Sensing

Butt

2024

Micromachines

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In this study, a comprehensive numerical analysis is conducted on a hybrid plasmonic waveguide (HPWG)-based racetrack ring resonator (RTRR) structure, tailored specifically for refractive index sensing applications. The sensor design optimization yields remarkable results, achieving a sensitivity of 275.7 nm/RIU. Subsequently, the boundaries of sensor performance are pushed even further by integrating a subwavelength grating (SWG) structure into the racetrack configuration, thereby augmenting the light–matter interaction. Of particular note is the pivotal role played by the length of the SWG segment in enhancing device sensitivity. It is observed that a significant sensitivity enhancement can be obtained, with values escalating from 377.1 nm/RIU to 477.7 nm/RIU as the SWG segment length increases from 5 µm to 10 µm, respectively. This investigation underscores the immense potential of HPWG in tandem with SWG for notably enhancing the sensitivity of photonic sensors. These findings not only advance the understanding of these structures but also pave the way for the development of highly efficient sensing devices with unprecedented performance capabilities.

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Section: Introductionmentioning

confidence: 99%

Racetrack Ring Resonator-Based on Hybrid Plasmonic Waveguide for Refractive Index Sensing

Butt

2024

Micromachines

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“…DL learns rules for inputs and outputs from large amounts of data, enabling the construction of non-linear models for various applications. Deep learning optimization methods can be applied in the fields of metasurface device design, including sensor [22][23][24][25], demultiplexer [26,27], coupler [28,29], inferometer [30], etc, to improve their design efficiency.The use of neural networks to implement data-driven models provides a new approach for the design of electromagnetic structures [31][32][33][34][35][36], such as EIT [37], broadband absorption [38] and perfect absorption [39]. Deep learning uses neural networks to learn patterns in data, and after training and optimizing on a dataset of metasurfaces, neural networks can effectively predict the best metasurface design.…”

Section: Introductionmentioning

confidence: 99%

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

Zhu,

Du,

Dong

et al. 2024

Phys. Scr.

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In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, and fully-connection layer network) is proposed. The metasurface structural parameters can be input into the forward prediction network to predict the corresponding absorption spectra. Meantime, the metasurface structural parameters can be obtained by inputting the absorption spectra into the inverse prediction network. Specially, in the inverse prediction network, the bidirectional long-short-term memory (BiLSTM) network can effectively capture the context relationship between absorption spectral sequence data, and the attention mechanism can enhance the BiLSTM output sequence features, which highlight the critical feature information. After the training, the mean square error (MSE) value on the validation set of the reverse prediction network converges to 0.0046, R2 reaches 0.975, and our network can accurately predict the metasurface structure parameters within 1.5s with a maximum error of 0.03mm. Moreover, this model can achieve the optimal design of multi-band metasurface absorbers, including the single-band, dual-band, and three-band absorptions. The proposed method can also be extended to other types of metasurface optimization design.

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“…The mode coupling theory, applicable to sensors like surface plasmon resonance (SPR) and grating sensors, is employed to calculate the resultant change in the effective refractive index [7][8][9]. This calculation method extends to devices such as microring resonators (MRRs) [10,11], Mach-Zehnder interferometers (MZIs) [12], multimode interferences (MMIs) [13,14], fiber Bragg gratings [15], photonic crystals [16], and other differential interferometers [17].…”

Section: Introductionmentioning

confidence: 99%

Dielectric Waveguide-Based Sensors with Enhanced Evanescent Field: Unveiling the Dynamic Interaction with the Ambient Medium for Biosensing and Gas-Sensing Applications—A Review

Butt

2024

Photonics

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Photonic sensors utilize light–matter interaction to detect physical parameters accurately and efficiently. They exploit the interaction between photons and matter, with light propagating through an optical waveguide, creating an evanescent field beyond its surface. This field interacts with the surrounding medium, enabling the sensitive detection of changes in the refractive index or nearby substances. By modulating light properties like intensity, wavelength, or phase, these sensors detect target substances or environmental changes. Advancements in this technology enhance sensitivity, selectivity, and miniaturization, making photonic sensors invaluable across industries. Their ability to facilitate sensitive, non-intrusive, and remote monitoring fosters the development of smart, connected systems. This overview delves into the material platforms and waveguide structures crucial for developing highly sensitive photonic devices tailored for gas and biosensing applications. It is emphasized that both the material platform and waveguide geometry significantly impact the sensitivity of these devices. For instance, utilizing a slot waveguide geometry on silicon-on-insulator substrates not only enhances sensitivity but also reduces the device’s footprint. This configuration proves particularly promising for applications in biosensing and gas sensing due to its superior performance characteristics.

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Racetrack Ring Resonator Integrated with Multimode Interferometer Structure Based on Low-Cost Silica–Titania Platform for Refractive Index Sensing Application

Cited by 28 publications

References 50 publications

Racetrack Ring Resonator-Based on Hybrid Plasmonic Waveguide for Refractive Index Sensing

Racetrack Ring Resonator-Based on Hybrid Plasmonic Waveguide for Refractive Index Sensing

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

Dielectric Waveguide-Based Sensors with Enhanced Evanescent Field: Unveiling the Dynamic Interaction with the Ambient Medium for Biosensing and Gas-Sensing Applications—A Review

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