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172 results on '"Sagor, Rakibul Hasan"'

25. Highly Sensitive Optically Tunable Transition Metal Nitride-Based Plasmonic Pressure Sensor With CMOS-Compatibility at Compact Subwavelength Dimensions

Author

Rahad, Rummanur, Haque, Mohammad Ashraful, Mahadi, Mahin Khan, Mohsin, Abu S. M., Faruque, Md. Omar, Afrid, Sheikh Mohd. Ta-Seen, Emon, Md. Jahidul Hoq, and Sagor, Rakibul Hasan

Abstract

This article presents a novel transition metal nitride (TMN)-based plasmonic pressure sensor (PPS) that utilizes a TMN–insulator–TMN structure. Our proposed sensor is built with zirconium nitride (ZrN), an alternative plasmonic material that provides several benefits over conventional plasmonic metals like silver and gold. Notably, ZrN is compatible with standard complementary metal oxide semiconductor (CMOS) technology and it offers tunability of optical properties. The sensor demonstrates a maximum pressure sensitivity of 177.56 nm/MPa with a resolution of $5.63\times 10^{-6}$ MPa. Furthermore, the incorporation of TMN endows the PPS with several beneficial characteristics, such as exceptional hardness, high-temperature thermal stability, optical tunability, and lower electrical resistivity which are typically absent in conventional plasmonic noble metals, limiting their practical application in plasmonic devices. Consequently, our proposed configuration outperforms the conventional noble material-based metal–insulator–metal (MIM) configuration, which removes the barriers to wide-scale adaptations of plasmonic devices by paving the way for the research and development of efficient, robust, and long-lasting sensors at subwavelength scales, thereby forging a link between nanoelectronics and plasmonics.

Published
2024
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36. Machine-Learning-Enabled Multimode Fiber Specklegram Sensors: A Review

Author

Newaz, Asif, Faruque, Md Omar, Mahmud, Rabiul Al, Sagor, Rakibul Hasan, and Khan, Mohammed Zahed Mustafa

Abstract

Multimode fiber (MMF) specklegram sensors have recently drawn significant attention due to the incorporation of machine learning (ML) algorithms in detecting different sensing parameters. Deep learning (DL) techniques provide an efficient way of extracting information from fiber specklegrams, which can be used in different sensing applications. Convolutional neural networks (CNNs) have proven to be extremely successful in imaging technologies over the past decade. The breakthrough from CNN has instigated new frontiers for applications in different domains. CNNs can automatically learn the variations in MMF specklegrams under different conditions. Besides detecting slight variations in the fiber, CNNs are also insusceptible to environmental noise and fluctuations, thus making them superior in terms of performance accuracy. They provide a low-cost and simple alternative to extracting information from fiber specklegrams which has piqued the interest of many researchers. In the past few years, there have been a growing number of research articles studying the applicability of DL frameworks in various sensing technologies. In this article, we present a comprehensive review of such articles that explore the use of ML in different MMF sensing applications, such as bending sensors, endoscopes, tactile or position sensors, and others. The principle of specklegram in MMF, the data generation process, an overview of different DL approaches, and open challenges and future research directions have also been discussed in this article.

Published
2023
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