This study examines the solid-state approach, which is gaining increasing importance in aviation technologies, within the framework of a new system design paradigm. The solid-state concept is based on the realization of fundamental functions such as propulsion, sensing, steering, and communication using electronic, electromagnetic, and semiconductor-based structures without relying on moving and mechanical components. This approach offers significant advantages, particularly in eliminating mechanical wear, reducing maintenance requirements, extending system lifespan, and minimizing acoustic and electromagnetic signatures. In this study, the architectures of traditional unmanned aerial vehicles (UAVs) are first examined, and the vibration, noise, energy loss, and failure risks caused by their reliance on rotating propellers, brushless DC motors, servo-controlled aerodynamic surfaces, and mechanically scanned LiDAR or radar systems are evaluated. Considering these issues, alternative solutions offered by solid-state based drone systems are assessed. In this context, electroaerodynamic (EAD) propulsion systems are considered as an innovative approach that provides thrust generation without rotating parts, based on the principle of accelerating ionized airflows through electric fields. Similarly, solid-state LiDAR technologies, which do not require mechanical scanning, offer high-precision three-dimensional sensing capabilities using semiconductor-based light steering methods. In the field of communication and sensing, phased array antennas provide directional and adaptive communication without the need for moving parts, thanks to their electronic beam steering capabilities. In conclusion, it has been shown that the holistic solid-state approach enables the development of quiet, reliable, and low-maintenance aerial platforms, as well as paving the way for next-generation applications such as micro and nano-scale aerial vehicles, indoor operations, and swarm-based autonomous systems.

OZTURK E.

PhD, assistant professor, faculty of engineering, department of artificial intelligence and data engineering, Karadeniz Technical University, Trabzon, Türkiye

E-mail: ercumentozturk@ktu.edu.tr, https://orcid.org/0000-0001-9623-6955

NABIYEV V.

PhD, professor, faculty of engineering, department of computer engineering and head of department of artificial intelligence and data engineering, Karadeniz technical university, Trabzon, Turkey

E-mail: vasif@ktu.edu.tr, https://orcid.org/0000-0003-0314-8134

NASER A.

PhD, assistant professor, faculty of engineering and architecture, department of computer engineering, Avrasya university, Trabzon, Türkiye

E-mail: amirnaser.si@gmail.com, https://orcid.org/0000-0001-9675-2212

CAVDAR T.

PhD, professor, faculty of engineering, department of computer engineering, Karadeniz technical university, Trabzon, Turkey

E-mail: ulduz@ktu.edu.tr, https://orcid.org/0000-0003-3656-9592

1. Piesing M., The Secret History of Drones, 23 September 2024. Available: https://airandspace.si.edu/air-and-space-quarterly/issue-12/secret-history-of-drones.
2. Ion-Propelled Aircraft, Wikipedia. Available: https://en.wikipedia.org/wiki/Ion-propelled_aircraft. [Accessed 23 February 2026].
3. Tajmar M., Biefeld-brown effect: Misinterpretation of corona wind phenomena, AIAA journal, vol. 42, no. 2, pp. 315-318, 2004. DOI: https://doi.org/10.2514/1.9095
4. Fantel H., Major de Seversky’s Ion-Propelled Aircraft, Popular Mechanics, vol. 122, no. 2, pp. 58-61, 1964.
5. Nosratollahi M., Ahmadi A. and Khoshkhoo R., A Design Algorithm for Electroaerodynamic Propulsion System, Journal of Space Science and Technology, vol. 18, no. 1, pp. 66-77, 2025.
6. Xu H., He Y., Strobel K. L., Gilmore C. K., Kelley S. P., Sebastian T., Woolston M. R., Perreault D. J. and Barrett S. R., Flight of an aeroplane with solid-state propulsion, Nature, vol. 563, no. 7732, pp. 532-535, 2018. DOI: https://doi.org/10.1038/s41586-018-0707-9
7. Seversky A. P. D., Electrostatic ion thrusters characterised by the acceleration grid. United States Patent US3130945A, 31 August 1959.
8. Tinney C. E. and Sirohi J., Multirotor drone noise at static thrust, Aiaa Journal, vol. 56, no. 7, pp. 2816-2826, 2018. DOI: https://doi.org/10.2514/1.J056827
9. Behnam G. and Mueller M., Vibration-based propeller fault diagnosis for multicopters, in 2018 International Conference on Unmanned Aircraft Systems (ICUAS), 2018.
10. Zhang R., Lu Y., Xu X., Zhang H. and Guan K., Design and Performance Study of Small Multirotor UAVs with Adjunctive Folding-Wing Range Extender, Drones, vol. 9, no. 12, p. 877, 2025. DOI: https://doi.org/10.3390/drones9120877
11. Alkobi J., The Evolution of Drones: From Military to Hobby & Commercial, 15 January 2019. Available: https://percepto.co/the-evolution-of-drones-from-military-to-hobby-commercial/. [Accessed 23 February 2026].
12. Vaddi R. S., Guan Y., Mamishev A. and Novosselov I., Analytical Model for Electrohydrodynamic Thrust, arXiv e-prints, pp. arXiv--2002, 2020.
13. Trovato S., Terenzi R., Usuelli D. and Belan M., Wind tunnel testing and performance modeling of an atmospheric ion thruster, Journal of Physics D: Applied Physics, vol. 58, no. 1, p. 015201, 2025. DOI: https://doi.org/10.1088/1361-6463/ad7d9c
14. Vaddi R. S., Guan Y. and Mamishev A., Analytical model for electrohydrodynamic thrust, Proceedings. Mathematical, physical, and engineering sciences, vol. 476, no. 2241, p. 20200220, 2020. DOI: https://doi.org/10.1098/rspa.2020.0220
15. Chu J., A mighty wind, MIT News , 3 April 2013. Available: https://news.mit.edu/2013/ionic-thrusters-0403. [Accessed 23 February 2026].
16. Gilmore C. K. and Barrett S. R., Electrohydrodynamic thrust density using positive corona-induced ionic winds for in-atmosphere propulsion, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 471, no. 2175, 2015. DOI: https://doi.org/10.1098/rspa.2014.0912
17. Types of LIDAR, Leishen Intelligent System Co., Ltd. Available: https://www.lslidar.com/types-of-lidar/. [Accessed 23 February 2026].
18. Garcîa Gômez P., Royo S., Rodrigo N. and Casas J. R., Geometric model and calibration method for a solid-state LiDAR, Sensors, vol. 20, no. 10, p. 2898, 2020. DOI: https://doi.org/10.3390/s20102898
19. Altınöz B., Eken H., Cönger A. and Can S., Modelling inertial measurement unit error parameters for an unmanned air vehicle, Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 66, no. 1, pp. 64-81, 2024. DOI: https://doi.org/10.33769/aupse.1308165
20. Ericco, MEMS-IMU error analysis, 16 January 2024. Available: https://medium.com/@ericco741/mems-imu-error-analysis-1969c5010210. [Accessed 23 February 2026].

solid-state drones, electroaerodynamic propulsion (EAD), low-observable UAV systems, silent noise UAVs, low-noise UAVs