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Design and Simulation of a Reconfigurable Multifunctional Optical Sensor

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Abstract

Generally, in Visible Light Communication (VLC) systems, Position Sensitive Device (PSD) sensor is used only for tracking purposes. However, the tracking process functions until both transmitter and receiver align, thus the PSD sensor remains idle for a long time. This interval can be exploited to achieve other functions by modifying its architecture. In this paper, a modification of the PSD structure has been achieved to make it able to perform energy harvesting and data acquisition as well as tracking. As the PSD is mainly formed of an array of photodiodes, our main idea is to use transistors to switch between the two modes of operation of the photodiodes (photoconductive and photovoltaic). Furthermore, switching between the output pins could be achieved depending on the desired function. The proposed sensor can extend the battery lifetime, increase the integration and the functionality, and reduce the physical size of the system. Simulation using MATALB was performed to validate the concept of the proposed structure and its operation. The results showed that the proposed sensor works successfully and it can be considered for further manufacturing levels. The presented sensor might be used in Free Space Optical (FSO) communication like cube satellite, or even in Underwater Wireless Optical Communication (UWOC).

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, School of Electrical and Communication, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, 600062, Chennai, Tamil Nadu, India

    Shaher Dwik & G. Sasikala

  2. Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, 641105, Chennai, Tamil Nadu, India

    S. Natarajan

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  1. Shaher Dwik
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  2. G. Sasikala
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Correspondence to Shaher Dwik, G. Sasikala or S. Natarajan.

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Shaher Dwik, Sasikala, G. & Natarajan, S. Design and Simulation of a Reconfigurable Multifunctional Optical Sensor. Opt. Mem. Neural Networks 32, 147–157 (2023). https://doi.org/10.3103/S1060992X2302008X

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