AI-Driven Optical Metamaterial Sensors with BICMOS Integration for Secure Mobile IoT Networks
DOI:
https://doi.org/10.31838/NJAP/08.02.02Keywords:
Optical Metamaterials, BiCMOS Integration, AI-Driven Sensing, Secure IoT Networks, Plasmonic Sensors.Abstract
As the number of mobile Internet of Things networks continues to increase rapidly, there is a significant need for sensing systems that are not only very sensitive but also exceedingly secure and use negligible amounts of energy. It is possible that traditional sensor systems will not function well in surroundings that are not predictable because they do not always record data reliably and do not always recognize items in the right manner. This paper proposes an Artificial Intelligence-facilitated Optical Metamaterial Sensors paired with BiCMOS (Bipolar Complementary Metal-Oxide-Semiconductor) technology (AIMS-BiC) that have the potential to assist in addressing these issues. The proposed method utilizes deeply learned models that have been modified to enable real-time data examination and problem identification. This is possible because plasmonic metamaterials can alter their response to light. The sensors are a component of a BiCMOS architecture, which is a combination of complementary metal-oxide semiconductors, which have low-power logic, and bipolar transistors, which provide rapid analog performance. This method will enable the use of the Internet of Things in a manner that is risk-free, straightforward, and easy to expand. The results of laboratory simulations indicate that this system is 36% more sensitive than conventional CMOS-only systems and has 48% fewer false positives than those regular systems. The technology also ensures that robust encryption is used, and it allows for the possibility of risk reduction over time through the utilization of dynamic control based on artificial intelligence. The research findings indicated that integrating artificial intelligence (AI), biCMOS, and metamaterials would result in mobile Internet of Things (IoT) detection networks that are superior in terms of intelligence, safety, and efficiency.
References
1. Koondhar MA, Jamali AA, Ren XC, Laghari MU, Qureshi F, Anjum MR, Khan Y, Zhai Y, Zhu Y. Graphene-based plasmonic metamaterial perfect absorber for biosensing applications. Electronics. 2022 Mar 16;11(6):930. https://doi.org/10.3390/electronics11060930
2. Wekalao J, Alsalman O, Natraj NA, Surve J, Parmar J, Patel SK. Design of graphene metasurface sensor for efficient detection of COVID-19. Plasmonics. 2023 Dec;18(6):2335-45. https://doi.org/10.1007/s11468-023-01946-2
3. Saigre-Tardif C, Faqiri R, Zhao H, Li L, del Hougne P. Intelligent meta-imagers: From compressed to learned sensing. Applied Physics Reviews. 2022 Mar 1;9(1). https://doi.org/10.1063/5.0076022
4. Hu J, Zhang H, Di B, Han Z, Poor HV, Song L. Meta-material sensor based Internet of Things: Design, optimization, and implementation. IEEE Transactions on Communications. 2022 Jun 29;70(8):5645-62. https://doi.org/10.1109/TCOMM.2022.3187150
5. Elsayed HA, Wekalao J, Mehaney A, Alfassam HE, Abukhadra MR, Hajjiah A, Zoubi WA. Graphene metasurfaces biosensor for COVID-19 detection in the infra-red regime. Scientific Reports. 2025 Mar 12;15(1):8573. https://doi.org/10.1038/s41598-025-92991-w
6. Wekalao J, U AK, S G, Almawgani AH, Abdelrahman Ali YA, Manvani R, Patel SK. Graphene-based THz surface plasmon resonance biosensor for hemoglobin detection applicable in forensic science. Plasmonics. 2024 Aug;19(4):2141-54. https://doi.org/10.1007/s11468-023-02146-8
7. Maheshwari RU, AR J, Pandey BK, Pandey D. Innovative Quantum PlasmoVision-Based Imaging for Real-Time Deepfake Detection. Plasmonics. 2025 Mar 1:1-7. https://doi.org/10.1007/s11468-025-02846-3
8. Zhang Y, Fowler C, Liang J, Azhar B, Shalaginov MY, Deckoff-Jones S, An S, Chou JB, Roberts CM, Liberman V, Kang M. Electrically reconfigurable non-volatile metasurface using low-loss optical phase-change material. Nature Nanotechnology. 2021 Jun;16(6):661-6. https://doi.org/10.1038/s41565-021-00881-9
9. Wang A, Fu L. Nano-Functional Materials for Sensor Applications. Molecules. 2024 Nov 22;29(23):5515. https://doi.org/10.3390/molecules29235515
10. Ivanov A, Bykov I, Barbillon G, Mochalov K, Korzhov D, Kovalev A, Smyk A, Shurygin A, Sarychev AK. Plasmon localization and field enhancement in flexible metasurfaces. Physical Review Applied. 2024 Dec 1;22(6):064064. https://doi.org/10.1103/PhysRevApplied.22.064064
11. Ji J, Li J, Wang Z, Li X, Sun J, Wang J, Fang B, Chen C, Ye X, Zhu S, Li T. On-chip multifunctional metasurfaces with full-parametric multiplexed Jones matrix. Nature Communications. 2024 Sep 27;15(1):8271. https://doi.org/10.1038/s41467-024-52476-2
12. Chernyadiev AV, But DB, Ivonyak Y, Ikamas K, Lisauskas A. A CMOS-integrated terahertz near-field sensor based on an ultra-strongly coupled meta-atom. Scientific Reports. 2024 May 20;14(1):11483. https://doi.org/10.1038/s41598-024-61971-x
13. Mishu SJ, Rahman MA, Dhar N. Highly sensitive refractive index sensing with a dual-band optically transparent ITO-based perfect metamaterial absorber for biomedical applications. Heliyon. 2024 Mar 15;10(5). https://doi.org/10.1016/j.heliyon.2024.e26842
14. Hardt E, Chavarin CA, Gruessing S, Flesch J, Skibitzki O, Spirito D, Vita GM, Simone GD, Masi AD, You C, Witzigmann B. Quantitative protein sensing with germanium THz-antennas manufactured using CMOS processes. Optics Express. 2022 Oct 17;30(22):40265-76. https://doi.org/10.1364/OE.469496
15. Richter M, Loth Y, Wigger AK, Nordhoff D, Rachinger N, Weisenstein C, Bosserhoff AK, Bolívar PH. High specificity THz metamaterial-based biosensor for label-free transcription factor detection in melanoma diagnostics. Scientific Reports. 2023 Nov 24;13(1):20708. https://doi.org/10.1038/s41598-023-46876-5
16. Hu J, Zhang H, Di B, Han Z, Poor HV, Song L. Meta-material sensor based Internet of Things: Design, optimization, and implementation. IEEE Transactions on Communications. 2022 Jun 29;70(8):5645-62. https://doi.org/10.1109/TCOMM.2022.3187150
17. Weng J, Ding Y, Hu C, Zhu XF, Liang B, Yang J, Cheng J. Meta-neural-network for real-time and passive deep-learning-based object recognition. Nature communications. 2020 Dec 9;11(1):6309.https://doi.org/10.1038/s41467-020-19693-x
18. Hou S, Han L, Zhang S, Zhang L, Zhang K, Xiao K, Yang Y, Zhang Y, Wen Y, Mo W, Tan Y. On‐Chip Metamaterial‐Enhanced Mid‐Infrared Photodetectors with Built‐In Encryption Features. Advanced Science. 2025:2415518. https://doi.org/10.1002/advs.202415518
19. Yigci D, Ahmadpour A, Tasoglu S. AI‐Based Metamaterial Design for Wearables. Advanced Sensor Research. 2024 Mar;3(3):2300109. https://doi.org/10.1002/adsr.202300109
20. Luo X, Hu Y, Ou X, Li X, Lai J, Liu N, Cheng X, Pan A, Duan H. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light: Science & Applications. 2022 May 27;11(1):158. https://doi.org/10.1038/s41377-022-00844-2
21. Banerjee S, Ghosh I, Santini C, Mangini F, Citroni R, Frezza F. All-Metal Metamaterial-Based Sensor with Novel Geometry and Enhanced Sensing Capability at Terahertz Frequency. Sensors. 2025 Jan 16;25(2):507. https://doi.org/10.3390/s25020507
22. Wekalao J. High-sensitivity graphene-MoS₂ hybrid metasurface biosensor with machine learning optimization for hemoglobin detection. Plasmonics. 2025 Mar 13:1-4. https://doi.org/10.1007/s11468-025-02886-9
23. Geng W, Zhou Y, Huang K, Ying Y, Xie L. A colorimetric-bionic sensor for multimodal molecular sensing using hierarchical metamaterials. Device. 2025 Feb 13. https://doi.org/10.1016/j.device.2025.100708
24. Men K, Lian Z, Tu H, Zhao H, Wei Q, Jin Q, Mao C, Wei F. An all-dielectric metamaterial terahertz biosensor for cytokine detection. Micromachines. 2023 Dec 26;15(1):53. https://doi.org/10.3390/mi15010053
25. Chen MK, Liu X, Sun Y, Tsai DP. Artificial intelligence in meta-optics. Chemical Reviews. 2022 Jun 24;122(19):15356-413. https://doi.org/10.1021/acs.chemrev.2c00012
26. https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot
