Research and Characteristic Analysis of PIN Based on Simulation
Research Article
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Research and Characteristic Analysis of PIN Based on Simulation

Xiaozhuo Liu 1*
1 School of Electrical Engineering and Information,Southwest Petroleum University, Chengdu, China;
*Corresponding author: 1719670355@qq.com
Published on 27 June 2025
Volume Cover
ACE Vol.169
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-209-6
ISBN (Online): 978-1-80590-210-2
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Abstract

This research, based on Silvaco software, aims to investigate the operational characteristics of PIN photodetectors under different lighting conditions, in particular the current response in both dark and illuminated states. The behaviour of the PIN photodetector under reverse bias is simulated, with particular emphasis on the effect of the optical wavelength on the device response. The development history of the PIN photodetector is first reviewed, followed by a detailed analysis of its basic operating principles. Using Tonyplot simulations, the current response of the PIN photodetector under various reverse bias voltages is examined, with particular emphasis on its high-impedance characteristics in the dark state. Further experiments show that the PIN detector has the best photoelectric conversion efficiency and the strongest response in the green light wavelength range around 0.5μm. Finally, the distribution of electric potential and field inside the device confirms the accuracy and efficiency of the PIN detector's operating mechanism, where light excitation generates charge carriers. This provides a theoretical basis and data support for the operation of the PIN photodetector within a specific wavelength range.

Keywords:

PIN detector, optical wavelength, light excitation, electric potential, electric field

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Liu,X. (2025). Research and Characteristic Analysis of PIN Based on Simulation. Applied and Computational Engineering,169,25-34.

References

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Cite this article

Liu,X. (2025). Research and Characteristic Analysis of PIN Based on Simulation. Applied and Computational Engineering,169,25-34.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MSS 2025 Symposium: Machine Vision System

ISBN: 978-1-80590-209-6(Print) / 978-1-80590-210-2(Online)
Editor: Cheng Wang, Marwan Omar
Conference date: 5 June 2025
Series: Applied and Computational Engineering
Volume number: Vol.169
ISSN: 2755-2721(Print) / 2755-273X(Online)