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Sensor Analysis and Semiconductor Process Advances for Autonomous Vehicles

Received: 24 August 2022    Accepted: 9 September 2022    Published: 11 October 2022
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Abstract

The future of the automotive industry is highly dependent on the integration of electronics into vehicles, particularly as the deployment of advanced electric vehicles (EVs) with varying levels of autonomy have come to fruition. On-board sensors in today's automobiles, such as cameras, radars, lidars, and ultrasonic radars, provide detection and uniformity scenarios in various environments and weather conditions. New technologies have also been deployed, such as 3-D vision and global navigation satellite systems (GNSS). In addition, 5G networks are impacting the development of connected and autonomous vehicles (AVs) making them safer and smarter. The use of on-board sensors in vehicles requires testing, verification, and validation, in order to provide safety, stability, reliability, and precision. Integration of these various systems and networks will aid in the creation of the vehicle-to-everything (V2X) environment. Silicon based integrated circuit (IC) architecture, such as SiGe CMOS and BiCMOS have enabled scaling and cost reduction of advanced sensors. The semiconductor’s value stack of multiple IC architectures, cost advantages, reliability, and sensor fusion can be combined with 5G/mmWave networks for Silicon and Gallium Nitride (GaN) technologies. Advanced materials will also play a pivotal role in driving the further scaling of sensors. These approaches currently play a key role in the process and manufacture of CMOS and MOSFETS. In this paper, an analysis of the current state of advanced sensors is presented, along with semiconductor process advances for AVs. IC innovations such as system integration, sensor local systems, and sensor health are also covered.

Published in Journal of Electrical and Electronic Engineering (Volume 10, Issue 5)
DOI 10.11648/j.jeee.20221005.13
Page(s) 199-206
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Autonomous Vehicle (AV), Camera, Lidar, Radar, BiCMOS, SWIR, SiGe, GaN

References
[1] D. Jong Yeong, G. Velasco-Hernandez, J. Barry, and J. Walsh, “Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review”, Sensors, 21, 2021, p. 2140. doi: 10.3390/s21062140.
[2] F. Rosique, P. Navarro, C. Fernández and A. Padilla, "A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research", Sensors, 19, no. 3, 2019, p. 648. doi: 10.3390/s19030648.
[3] P. Hillger, J. Grzyb, R. Jain, and U. R. Pfeiffer, “Terahertz Imaging and Sensing Applications with Silicon-Based Technologies”, IEEE Transactions on Terahertz Science and Technology, 9, 2019, p. 1. doi: 10.1109/TTHZ.2018.2884852.
[4] G. Rudolph, and U. Voelzke, (2017). “Three Sensor Types Drive Autonomous Vehicles”, Fierce Electronics, https://www.fierceelectronics.com/components/three-sensor-types-drive-autonomous-vehicles [Accessed 13 July 2022].
[5] “Transparent Antennas”, https//metamaterial.com/solutions/transparent-antennas-2/ [Accessed 13 July 2022].
[6] S. Bertoldo, C. Lucianaz, and M. Allegretti, "77 GHz automotive anti-collision radar used for meteorological purposes", 2017 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), 2017, pp. 49-52. doi: 10.1109/APWC.2017.8062238.
[7] M. Giallorenzo, X. Cai, A. Nashashibi, and K. Sarabandi, "Radar Backscatter Measurements of Road Surfaces at 77 GHz," 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, 2018, pp. 2421-2422. doi: 10.1109/APUSNCURSINRSM.2018.8609191.
[8] S. Chatterjee. (2018). A 77 GHz BCB Based High Performance Antenna Array for Autonomous Vehicle Radars [Master’s thesis, University of Windsor]. https://scholar.uwindsor.ca/etd/7505
[9] J. Wojtanowski, M. Zygmunt, M. Kaszczuk, Z. Mierczyk, and M. Muzal, “Comparison of 905 nm and 1550 nm Semiconductor Laser Rangefinders’ Performance Deterioration Due to Adverse Environmental Conditions”, Opto-Electronics Review, 22, no. 3, 2014, pp. 183–190. doi: 10.2478/s11772-014-0190-2.
[10] International Standard IEC 60825-1. Safety of Laser Products—Part 1: Equipment Classification and Requirements; International Electrotechnical Commission, 2007.
[11] J. Vargas, S. Alsweiss, O. Toker, R. Razdan, and J. Santos, “An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions”, Sensors, 21, 2021, p. 5397. doi: 10.3390/s21165397.
[12] J. Van Brummelen, M. O’Brien, D. Gruyer and H. Najjaran, "Autonomous vehicle perception: The technology of today and tomorrow", Transportation Research Part C: Emerging Technologies, 89, 2018, pp. 384-406. doi: 10.1016/j.trc.2018.02.012.
[13] K. Sjöberg, P. Andres, T. Buburuzan and A. Brakemeier, " Cooperative Intelligent Transport Systems in Europe: Current Deployment Status and Outlook", IEEE Vehicular Technology Magazine, 12, no. 2, 2017, pp. 89-97. doi: 10.1109/MVT.2017.2670018.
[14] “GNSS/GPS Differences Explained”, http://www.terrisgps.com/gnss-gps-differences-explained/ [Accessed 15 Jun. 2019].
[15] M. Hadj-Bachir and P. Souza, "LIDAR Sensor Simulation in Adverse Weather Condition for Driving Assistance Development”, HAL Open Science, 2019. https://hal.archives-ouvertes.fr/hal-01998668 [Accessed 5 August 2022].
[16] S. Patole, M. Torlak, D. Wang, and M. Ali, "Automotive Radars: A Review of Signal Processing Techniques," IEEE Signal Processing Magazine, 34, no. 2, 2017, pp. 22-35. doi: 10.1109/MSP.2016.2628914.
[17] J. Steinbaeck, C. Steger, G. Holweg and N. Druml, "Next Generation Radar Sensors in Automotive Sensor Fusion Systems", 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2017, pp. 1-6. doi: 10.1109/SDF.2017.8126389.
[18] B. Stark, M. McGee, and Y. Chen, "Short Wave Infrared (SWIR) Imaging Systems Using Small Unmanned Aerial Systems (sUAS)”, 2015 International Conference on Unmanned Aircraft Systems (ICUAS), 2015, pp. 495-501. doi: 10.1109/ICUAS.2015.7152328.
[19] M. Bozanic and S Sinha, “Emerging Transistor Technologies Capable of Terahertz Amplification: A Way to Re-Engineer Terahertz Radar Sensors”, Sensors, 19, no. 11, 2019, p. 2454. doi: 10.3390/s19112454.
[20] Y. Kawano, T Suzuki, M. Sato, T. Horosi, K. Joshin, "A 77 Ghz Transceiver in 90 nm CMOS”, 2009 IEEE Solid-State Circuits (ISSCC), 2009, pp. 310-311. doi: 10.1109/ISSCC.2009.4977432.
[21] A. Mantooth, C-M. Zetterling, and A. Rusu, “Venus Calling Silicon Carbide Radio Circuits Can Take the Heat Needed to Phone Home from Our Hellish Sister Planet”, IEEE Spectrum, 58, no. 5, 2021, pp. 24-30. doi: 10.1109/MSPEC.2021.9423815.
[22] G. Jessen, “The Supercharged Semiconductor: Gallium Oxide Could Make Powerful Radios and Switch Thousands of Volts”, IEEE Spectrum, 58, no. 4, 2021, pp. 36-42. doi: 10.1109/MSPEC.2021.9393994.
Cite This Article
  • APA Style

    Jorge Vargas, Antonio Saavedra. (2022). Sensor Analysis and Semiconductor Process Advances for Autonomous Vehicles. Journal of Electrical and Electronic Engineering, 10(5), 199-206. https://doi.org/10.11648/j.jeee.20221005.13

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    ACS Style

    Jorge Vargas; Antonio Saavedra. Sensor Analysis and Semiconductor Process Advances for Autonomous Vehicles. J. Electr. Electron. Eng. 2022, 10(5), 199-206. doi: 10.11648/j.jeee.20221005.13

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    AMA Style

    Jorge Vargas, Antonio Saavedra. Sensor Analysis and Semiconductor Process Advances for Autonomous Vehicles. J Electr Electron Eng. 2022;10(5):199-206. doi: 10.11648/j.jeee.20221005.13

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  • @article{10.11648/j.jeee.20221005.13,
      author = {Jorge Vargas and Antonio Saavedra},
      title = {Sensor Analysis and Semiconductor Process Advances for Autonomous Vehicles},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {10},
      number = {5},
      pages = {199-206},
      doi = {10.11648/j.jeee.20221005.13},
      url = {https://doi.org/10.11648/j.jeee.20221005.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20221005.13},
      abstract = {The future of the automotive industry is highly dependent on the integration of electronics into vehicles, particularly as the deployment of advanced electric vehicles (EVs) with varying levels of autonomy have come to fruition. On-board sensors in today's automobiles, such as cameras, radars, lidars, and ultrasonic radars, provide detection and uniformity scenarios in various environments and weather conditions. New technologies have also been deployed, such as 3-D vision and global navigation satellite systems (GNSS). In addition, 5G networks are impacting the development of connected and autonomous vehicles (AVs) making them safer and smarter. The use of on-board sensors in vehicles requires testing, verification, and validation, in order to provide safety, stability, reliability, and precision. Integration of these various systems and networks will aid in the creation of the vehicle-to-everything (V2X) environment. Silicon based integrated circuit (IC) architecture, such as SiGe CMOS and BiCMOS have enabled scaling and cost reduction of advanced sensors. The semiconductor’s value stack of multiple IC architectures, cost advantages, reliability, and sensor fusion can be combined with 5G/mmWave networks for Silicon and Gallium Nitride (GaN) technologies. Advanced materials will also play a pivotal role in driving the further scaling of sensors. These approaches currently play a key role in the process and manufacture of CMOS and MOSFETS. In this paper, an analysis of the current state of advanced sensors is presented, along with semiconductor process advances for AVs. IC innovations such as system integration, sensor local systems, and sensor health are also covered.},
     year = {2022}
    }
    

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Author Information
  • Department of Engineering Technology Faculty, Middle Tennessee State University, Murfreesboro, USA

  • Department of Engineering Technology Faculty, Middle Tennessee State University, Murfreesboro, USA

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