Autors: Mladenov, V. M. Title: Advanced Memristor Modeling: Memristor Circuits and Networks Keywords: modeling; memristor; window function; titanium dioxide; nonl Abstract: The investigation of new memory schemes, neural networks, computer systems and many other improved electronic devices is very important for future generations of electronic circuits and for their widespread application in all the areas of industry. Relatedly, the analysis of new efficient and advanced electronic elements and circuits is an essential field of highly developed electrical and electronic engineering. The resistance-switching phenomenon, observed in many amorphous oxides, has been investigated since 1970 and is promising for inclusion in technologies for constructing new electronic memories. It has been established that such oxide materials have the ability to change their conductance in accordance to the applied voltage and memorizing their state for a long time interval. Similar behavior was predicted for the memristor element by Leon Chua in 1971. References Issue
|
Цитирания (Citation/s):
1. J.A.Tenreiro Machado, António M.Lopes. Multidimensional scaling locus of memristor and fractional order elements., Journal of Advanced Research Volume 25, September 2020, https://doi.org/10.1016/j.jare.2020.01.004, pp. 147-157. - 2020 - в издания, индексирани в Scopus или Web of Science
2. Yongbin Yu; Kwabena Adu; Nyima Tashi; Patrick Anokye; Xiangxiang Wang; Mighty Abra Ayidzoe., RMAF: Relu-Memristor-Like Activation Function for Deep Learning., IEEE Access ( Volume: 8), DOI: 10.1109/ACCESS.2020.2987829, pp. 72727 - 72741. - 2020 - в издания, индексирани в Scopus или Web of Science
3. O. O. Permyakova & A. E. Rogozhin, Simulation of Resistive Switching in Memristor Structures Based on Transition Metal Oxides., Russian Microelectronics, volume 49, https://doi.org/10.1134/S106373972004006X, pp. 303–313, 2020. - 2020 - в издания, индексирани в Scopus или Web of Science
4. Elena Solovyeva, Steffen Schulze, Hanna Harchuk. Behavioral Modeling of Memristor-Based Rectifier Bridge., Appl. Sci. 2021, 11, 2908. https://doi.org/10.3390/app11072908, pp. 1 - 15. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
5. Kirilov, S. and Zaykov, I. (2020), "Analysis of memristor-based differentiating circuit", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 39 No. 3, pp. 683-690. https://doi.org/10.1108/COMPEL-10-2019-0389 - 2020 - в издания, индексирани в Scopus или Web of Science
6. Pawar, H.S., 2021. Memristors and Their Applications. In Nanoelectronic Devices for Hardware and Software Security (pp. 101-117). CRC Press. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
7. Kirilov, S., I. Zaykov., “A Neural Network with HfO2 Memristors”, Proc. Tech. Univ. of Sofia, ISSN: 1311-0829, Vol.. 71, No. 1, 2021. - 2021 - в български издания
8. 852) Jourdana, C., Jüngel, A. and Zamponi, N., 2022. Three-species drift-diffusion models for memristors. arXiv preprint arXiv:2204.03275. (Google Scholar) - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
9. Kirilov, S., I. Zaykov, “A metal oxide memristor-based oscillators and filters”, Proceedings of Technical University of Sofia, ISSN: 2738-8549, 2022, VOL. 72, NO. 2, https://doi.org/10.47978/TUS.2022.72.02.006, pp. 32 – 37. (Google Scholar) - 2022 - в български издания
10. Zaykov, I., “A modified metal-oxide memristor model for reconfigurable filters”, 2022, Proceedings of Technical University of Sofia, ISSN: 2738-8549, VOL. 72, NO. 2, https://doi.org/10.47978/TUS.2022.72.02.005, pp. 27 – 31. (Google Scholar) - 2022 - в български издания
11. Jourdana, C., Jüngel, A. and Zamponi, N., 2022. Three-species drift-diffusion models for memristors.,arXiv:2204.03275., pp. 1-41, https://doi.org/10.48550/arXiv.2204.03275 (Google Scholar) - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
12. Kyurkchiev, N. and Iliev, A., 2022. On a Hypothetical Model with Second Kind Chebyshev’s Polynomial–Correction: Type of Limit Cycles, Simulations, and Possible Applications. Algorithms, vol. 15, issue (12), pp. 1 – 14, https://doi.org/10.3390/a15120462, ISSN:1999-4893, (Scopus, Web of Science, Google Scholar) SJR 0.515 - 2022 - в издания, индексирани в Scopus или Web of Science
13. Jüngel, A. and Vetter, M., 2023. Degenerate drift-diffusion systems for memristors. arXiv preprint arXiv:2311.16591., pp. 1-29 (Google Scholar) - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
14. Herda, M., Jüngel, A., & Portisch, S. (2024). “Charge transport systems with Fermi-Dirac statistics for memristors,” arXiv preprint arXiv:2409.01196. pp. 1-32, https://doi.org/10.48550/arXiv.2409.01196 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
15. Bednarz, K. and Garda, B., 2024. “Measurement and Modeling of Self-Directed Channel (SDC) Memristors: An Extensive Study,” Energies, vol. 17, issue (21), pp. 1-20, https://doi.org/10.3390/en17215400 (Web of Science, Scopus, Google Scholar) IF 3.0, SJR 0.651 - 2024 - в издания, индексирани в Scopus или Web of Science
Вид: монография/части от монография, индексирана в Google Scholar