Neural Networks in Optimizing the Performance of the Elliptical-Plasmonic Sensor

Khaikal Ramadhan; Andi M. N. F. Syamsul; Arip Marwan; Beny Agustirandi; Mhd Yasir; Hadi Christian

doi:10.28991/ESJ-2024-08-05-07

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Khaikal Ramadhan
20222307@mahasiswa.itb.ac.id
Department of Physics, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung 40132,, Indonesia https://orcid.org/0000-0001-8673-7799
Andi M. N. F. Syamsul Department of Physics, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung 40132,, Indonesia
Arip Marwan Department of Electrical Engineering, Faculty of Engineering, University of Riau, Pekanbaru, 28293,, Indonesia
Beny Agustirandi Department of Physics, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung 40132,, Indonesia
Mhd Yasir Department of Physics, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung 40132,, Indonesia
Hadi Christian Department of Engineering Physics, Faculty of Engineering, Institut Teknologi Bandung, Bandung 40116,, Indonesia

Vol. 8 No. 5 (2024): October

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In this work, we report the capability of a PCF-SPR sensor with an elliptical core, which has high sensitivity, and it is explained using a machine learning approach. The sensor component consists of fused silica as the background material, TiO₂ as the adhesive material between the dielectric material and the plasmonic material, and Au was chosen as plasmonic material with optimal thicknesses of 35 nm for TiO₂and 45 nm for Au. Numerical results show that the sensor component has a high sensitivity of 24,000 nm/RIU for four modes that have consistent shifts, including x-polarized, x-odd, y-polarized, and y-odd. Meanwhile, AS maximums were found of -91.82 1/RIU for x-polarized, -91.88 1/RIU for y-polarized, -90.98 1/RIU for x-odd, and -89.276 1/RIU for y-odd respectively, on the refractive index of the analyte of 1,365 RIU. The ML algorithm was used to optimize the sensor parameters, and it was found that the algorithm had a very low MSE of 0.00083; this result is better than the previous report work.

Doi: 10.28991/ESJ-2024-08-05-07

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Ramadhan, K., Syamsul, A. M. N. F., Marwan, A., Agustirandi, B., Yasir, M., & Christian, H. (2024). Neural Networks in Optimizing the Performance of the Elliptical-Plasmonic Sensor. Emerging Science Journal, 8(5), 1798–1811. https://doi.org/10.28991/ESJ-2024-08-05-07

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Amendola, V., Pilot, R., Frasconi, M., Maragò, O. M., & Iatí¬, M. A. (2017). Surface plasmon resonance in gold nanoparticles: A review. Journal of Physics Condensed Matter, 29(20), 203002. doi:10.1088/1361-648X/aa60f3.

Nuzhat, S., Sultana, S., Hassan, M. F. Bin, Biswas, S. K., Das Gupta, M., & Talukder, H. (2021). Dual scaled approach SPR-based PCF RI sensor with ultra-low loss. Journal of Physics: Conference Series, 2070(1), 012109. doi:10.1088/1742-6596/2070/1/012109.

Sakib, M. N., Hossain, M. B., Al-tabatabaie, K. F., Mehedi, I. M., Hasan, M. T., Hossain, M. A., & Amiri, I. S. (2019). High performance dual core D-shape PCF-SPR sensor modeling employing gold coat. Results in Physics, 15. doi:10.1016/j.rinp.2019.102788.

Liu, W., Hu, C., Zhou, L., Yi, Z., Liu, C., Lv, J., Yang, L., & Chu, P. K. (2022). A square-lattice D-shaped photonic crystal fiber sensor based on SPR to detect analytes with large refractive indexes. Physica E: Low-Dimensional Systems and Nanostructures, 138. doi:10.1016/j.physe.2021.115106.

Ibrahimi, K. M., Kumar, R., & Pakhira, W. (2023). Enhance the Design and Performance Analysis of a Highly Sensitive Twin-Core PCF SPR Biosensor with Gold Plating for the Early Detection of Cancer Cells. Plasmonics, 18(3), 995–1006. doi:10.1007/s11468-023-01825-w.

Guo, T., Zhang, T., Li, Y., & Qiao, X. (2020). Highly Sensitive FBG Seismometer with a 3D-Printed Hexagonal Configuration. Journal of Lightwave Technology, 38(16), 4588–4595. doi:10.1109/JLT.2020.2991667.

Irawan, D., Ramadhan, K., Saktioto, T., & Marwin, A. (2023). An optimum design of high sensitivity PMMA-coated FBG sensor for temperature measurement. Telkomnika (Telecommunication Computing Electronics and Control), 21(2), 382–389. doi:10.12928/TELKOMNIKA.v21i2.22746.

Lee, B. (2003). Review of the present status of optical fiber sensors. Optical Fiber Technology, 9(2), 57–79. doi:10.1016/S1068-5200(02)00527-8.

Tosi, D., Macchi, E. G., Gallati, M., Braschi, G., Cigada, A., Rossi, S., Leen, G., & Lewis, E. (2014). Fiber-optic chirped FBG for distributed thermal monitoring of ex-vivo radiofrequency ablation of liver. Biomedical Optics Express, 5(6), 1799. doi:10.1364/boe.5.001799.

Yang, X., Yuan, Y., Dai, Z., Liu, F., & Huang, J. (2016). Optical property and adsorption isotherm models of glucose sensitive membrane based on prism SPR sensor. Sensors and Actuators, B: Chemical, 237, 150–158. doi:10.1016/j.snb.2016.06.090.

Horváth, R., Pedersen, H. C., Skivesen, N., Selmeczi, D., & Larsen, N. B. (2003). Optical waveguide sensor for on-line monitoring of bacteria. Optics Letters, 28(14), 1233. doi:10.1364/ol.28.001233.

Wu, T., Shao, Y., Wang, Y., Cao, S., Cao, W., Zhang, F., Liao, C., He, J., Huang, Y., Hou, M., & Wang, Y. (2017). Surface plasmon resonance biosensor based on gold-coated side-polished hexagonal structure photonic crystal fiber. Optics Express, 25(17), 20313. doi:10.1364/oe.25.020313.

Irawan, D., Ramadhan, K., Saktioto, Fitmawati, Hanto, D., Widiyatmoko, B., Marwin, A., & Azhar. (2023). Ultra-low loss and dual polarized SPR-PCF sensor based on refractive index. Bulletin of Electrical Engineering and Informatics, 12(6), 3325–3334. doi:10.11591/eei.v12i6.4293.

Sen, S., Hasan, M. M., & Ahmed, K. (2021). Ultra-Low Material Loss Quasi Pattern Based Photonic Crystal Fiber for Long Distance THz Wave Propagation. Silicon, 13(5), 1663–1673. doi:10.1007/s12633-020-00554-7.

Mahabubur Rahman, M., Aslam Molla, M., Kumar Paul, A., Based, M. A., Masud Rana, M., & Anower, M. S. (2020). Numerical investigation of a highly sensitive plasmonic refractive index sensor utilizing hexagonal lattice of photonic crystal fiber. Results in Physics, 18. doi:10.1016/j.rinp.2020.103313.

Falah, A. A. S., Wong, W. R., & Mahamd Adikan, F. R. (2022). Single-mode eccentric-core D-shaped photonic crystal fiber surface plasmon resonance sensor. Optics and Laser Technology, 145. doi:10.1016/j.optlastec.2021.107474.

Bing, P., Sui, J., Wu, G., Guo, X., Li, Z., Tan, L., & Yao, J. (2020). Analysis of Dual-Channel Simultaneous Detection of Photonic Crystal Fiber Sensors. Plasmonics, 15(4), 1071–1076. doi:10.1007/s11468-020-01131-9.

Yasli, A., Ademgil, H., Haxha, S., & Aggoun, A. (2020). Multi-Channel Photonic Crystal Fiber Based Surface Plasmon Resonance Sensor for Multi-Analyte Sensing. IEEE Photonics Journal, 12(1). doi:10.1109/JPHOT.2019.2961110.

Tahhan, S. R., & Taha, R. M. (2022). Mercedes Benz logo based plasmon resonance PCF sensor. Sensing and Bio-Sensing Research, 35. doi:10.1016/j.sbsr.2021.100468.

Ramola, A., Marwaha, A., & Singh, S. (2021). Design and investigation of a dedicated PCF SPR biosensor for CANCER exposure employing external sensing. Applied Physics A: Materials Science and Processing, 127(9), 643. doi:10.1007/s00339-021-04785-2.

Hoseinian, M. S., Ahmadi, A., Safaei Bezgabadi, A., & Bolorizadeh, M. A. (2021). Simulation of wagon wheel optical fiber biosensor for quick and easy detection of cancer cells. Optical and Quantum Electronics, 53(8), 427. doi:10.1007/s11082-021-02970-4.

Kumar, A., Verma, P., & Jindal, P. (2023). Surface plasmon resonance sensor based on MXene coated PCF for detecting the cancer cells with machine learning approach. Microelectronic Engineering, 267–268. doi:10.1016/j.mee.2022.111897.

Singh, S., & Prajapati, Y. K. (2023). Novel Bottom-Side Polished PCF-Based Plasmonic Biosensor for Early Detection of Hazardous Cancerous Cells. IEEE Transactions on Nanobioscience, 22(3), 647–654. doi:10.1109/TNB.2023.3233990.

Ehyaee, A., Mohammadi, M., Seifouri, M., & Olyaee, S. (2023). Design and numerical investigation of a dual-core photonic crystal fiber refractive index sensor for cancer cells detection. European Physical Journal Plus, 138(2), 129. doi:10.1140/epjp/s13360-023-03749-0.

Mittal, S., Saharia, A., Ismail, Y., Petruccione, F., Bourdine, A. V., Morozov, O. G., Demidov, V. V., Yin, J., Singh, G., & Tiwari, M. (2023). Spiral Shaped Photonic Crystal Fiber-Based Surface Plasmon Resonance Biosensor for Cancer Cell Detection. Photonics, 10(3), 230. doi:10.3390/photonics10030230.

Yan, X., Wang, Y., Cheng, T., & Li, S. (2021). Photonic crystal fiber SPR liquid sensor based on elliptical detective channel. Micromachines, 12(4), 408. doi:10.3390/mi12040408.

Otupiri, R., Akowuah, E. K., & Haxha, S. (2015). Multi-channel SPR biosensor based on PCF for multi-analyte sensing applications. Optics Express, 23(12), 15716. doi:10.1364/oe.23.015716.

Li, W., Chen, Y., Xu, J., Jiang, M., & Zou, H. (2023). A D-Shaped SPR-Based PCF Sensor with an Extremely High-Amplitude Sensitivity for Measuring the Refractive Index. Micromachines, 14(7), 1295. doi:10.3390/mi14071295.

Kalyoncu, C., Yasli, A., & Ademgil, H. (2022). Machine learning methods for estimating bent photonic crystal fiber based SPR sensor properties. Heliyon, 8(11), e11582. doi:10.1016/j.heliyon.2022.e11582.

Li, H., Chen, H., Li, Y., Chen, Q., Fan, X., Li, S., & Ma, M. (2023). Prediction of the optical properties in photonic crystal fiber using support vector machine based on radial basis functions. Optik, 275, 170603. doi:10.1016/j.ijleo.2023.170603.

Kumar, A., Verma, P., & Jindal, P. (2023). Machine learning approach to surface plasmon resonance sensor based on MXene coated PCF for malaria disease detection in RBCs. Optik, 274, 170549. doi:10.1016/j.ijleo.2023.170549.

Dogan, Y., Katirci, R., Erdogan, İ., & Yartasi, E. (2023). Artificial neural network based optimization for Ag grated D-shaped optical fiber surface plasmon resonance refractive index sensor. Optics Communications, 534, 129332. doi:10.1016/j.optcom.2023.129332.

Brixner, B. (1967). Refractive-Index Interpolation for Fused Silica*. Journal of the Optical Society of America, 57(5), 674. doi:10.1364/josa.57.000674.

Sehmi, H. S., Langbein, W., & Muljarov, E. A. (2017). Optimizing the Drude-Lorentz model for material permittivity: Method, program, and examples for gold, silver, and copper. Physical Review B, 95(11), 115444. doi:10.1103/PhysRevB.95.115444.

Kumar, D., Khurana, M., Sharma, M., & Singh, V. (2023). Analogy of gold, silver, copper and aluminium based ultra-sensitive surface plasmon resonance photonic crystal fiber biosensors. Materials Today: Proceedings, 1-6. doi:10.1016/j.matpr.2023.02.319.

Divya, J., Selvendran, S., Raja, A. S., & Borra, V. (2024). A Novel Plasmonic Sensor Based on Dual-Channel D-Shaped Photonic Crystal Fiber for Enhanced Sensitivity in Simultaneous Detection of Different Analytes. IEEE Transactions on Nanobioscience, 23(1), 127–139. doi:10.1109/TNB.2023.3294330.

Majeed, M. F., & Ahmad, A. K. (2024). Design and analysis of a high sensitivity open microchannel PCF-based surface plasmon resonance refractometric sensor. Optical Materials, 147, 114617. doi:10.1016/j.optmat.2023.114617.

Singh, S., & Prajapati, Y. K. (2020). Dual-polarized ultrahigh sensitive gold/MoS2/graphene based D-shaped PCF refractive index sensor in visible to near-IR region. Optical and Quantum Electronics, 52(1), 17. doi:10.1007/s11082-019-2122-3.

Tian, M., Li, J., & Meng, F. (2023). Independent measurement of refractive index and temperature using D-gapped dual-channel structure in a photonic crystal fiber. Optical and Quantum Electronics, 55(4), 301. doi:10.1007/s11082-023-04616-z.

Ibrahimi, K. M., Kumar, R., & Pakhira, W. (2023). A graphene/Au/TiO2 coated dual-core PCF SPR biosensor with improved design and performance for early cancer cell detection of with high sensitivity. Optik, 288. doi:10.1016/j.ijleo.2023.171186.

Sarker, H., & Faisal, M. (2023). Surface plasmon resonance sensor using photonic crystal fiber for sucrose detection. Sensing and Bio-Sensing Research, 39. doi:10.1016/j.sbsr.2022.100544.

Sarker, H., Alam, F., Khan, M. R., Mollah, M. A., Hasan, M. L., & Rafi, A. B. M. S. (2022). Designing highly sensitive exposed core surface plasmon resonance biosensors. Optical Materials Express, 12(5), 1977. doi:10.1364/ome.452096.

Islam, M. R., Iftekher, A. N. M., Meraz, M. H. I., Nayen, M. J., & Khan, M. R. H. (2023). Design of a dual arrow shaped and dual plasmonic material compatible SPR PCF sensor. Optical and Quantum Electronics, 55(13), 1125. doi:10.1007/s11082-023-05364-w.

Piliarik, M., & Homola, J. (2009). Surface plasmon resonance (SPR) sensors: approaching their limits? Optics Express, 17(19), 16505. doi:10.1364/oe.17.016505.

Agarap, A. F. (2018). Deep Learning using Rectified Linear Units (ReLU). arXiv Preprint, arXiv:1803.08375. doi:10.48550/arXiv.1803.08375.

de Amorim, L. B. V., Cavalcanti, G. D. C., & Cruz, R. M. O. (2023). The choice of scaling technique matters for classification performance. Applied Soft Computing, 133. doi:10.1016/j.asoc.2022.109924.

Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M., & Khorasani, N. (2021). The evaluation of artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration. Urban Climate, 37. doi:10.1016/j.uclim.2021.100837.

Verma, P., Kumar, A., & Jindal, P. (2022). Machine Learning Approach for SPR based Photonic Crystal Fiber Sensor for Breast Cancer Cells Detection. 2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI), 54, 7–12. doi:10.1109/rtsi55261.2022.9905187.

Meng, F., Ding, J., Zhao, Y., Liu, H., Su, W., Yang, L., Tao, G., Pryamikov, A., Wang, X., Mu, H., Niu, Y., He, J., Zhang, X., Lou, S., Sheng, X., & Liang, S. (2023). Artificial intelligence designer for optical Fibers: Inverse design of a Hollow-Core Anti-Resonant fiber based on a tandem neural network. Results in Physics, 46. doi:10.1016/j.rinp.2023.106310.

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Neural Networks in Optimizing the Performance of the Elliptical-Plasmonic Sensor

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Neural Networks in Optimizing the Performance of the Elliptical-Plasmonic Sensor

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