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Bulanık Mantık Tasarımcısı ile Memristor Histerezis Eğrisinin Tahmini

Year 2022, , 64 - 72, 25.04.2022
https://doi.org/10.53433/yyufbed.1070358

Abstract

Bu çalışmada memristör taklit devresi olarak doğrusal, katkılı sürüklenme TiO2 modeli kullanılarak bulanık mantık tasarımcısı aracı ile memristör histerezis eğrisinin tahmin edilebilir bölgesinin optimize edilmesi amaçlanmıştır. Bu model kullanılarak memristör karakteristiğinin akım-gerilim (I-V) eğrisi yani histerezis döngüsü oluşturulmuştur. Analog uygulama çalışmalarında özellikle filtre devrelerinde belirli bir frekans aralığında histerezis döngüsünü elde edebilmek filtrenin kalitesinde önemli değişikliklere yol açmaktadır. Bu noktada en uygun parametre noktaları için çeşitli deneme yanılma testleri yapıldı. Burada, optimum histerezis döngü parametreleri, bulanık mantık tasarımcısı kullanılarak belirlendi. Sonuç olarak akım-voltaj ve frekans bilgisine bağlı olarak optimum histerezis döngüsü hakkında daha pratik ve daha kararlı sonuçlar elde edildi. Bu sayede kullanıcılar istenilen amaca ve ihtiyaca göre parametreleri belirleyerek daha öngörülebilir tepkiler alabilmektedir. Özellikle analog ve digital elektronik uygulama çalışmalarında hem zaman kaybını hem de çalışılabilir bölgeyi rahat belirleyerek sonuca daha kararlı yaklaşabilmektedirler.

References

  • Abdel-Kader, R. F., & Abuelenin, S. M. (2015). Memristor model based on fuzzy window function. IEEE International Conference on Fuzzy Systems (FUZZY-IEEE),1-5. doi: 10.1109/FUZZ-IEEE.2015.7338105
  • Biolek, Z., Biolek, D., & Biolkova, V. (2009). SPICE model of memristor with nonlinear dopant drift. Radio engineering, 18.
  • Chua, L. O., & Kang, S. M. (1976). Memristive devices and systems. Proceedings of the IEEE, 64, 209-223. doi:10.1109/PROC.1976.10092
  • Görgülü, Ö., & Bek, S. Ş. Y. (2017). Analysis of Fuzzy Logic Applications via Matlab.
  • Joglekar, Y. N., & Wolf, S. J. (2009). The elusive memristor: properties of basic electrical circuits. European Journal of physics, 30, 661. doi: 10.1088/0143-0807/30/4/001/meta
  • Kim, H., Sah, M. P., Yang, C., Cho, S., & Chua, L. O. (2012). Memristor emulator for memristor circuit applications. IEEE Transactions on Circuits and Systems I: Regular Papers, 59, 2422-2431. doi: 10.1109/TCSI.2012.2188957
  • Lavanya, K., Durai M. S., & Iyengar, N. C. (2011). Fuzzy rule based inference system for detection and diagnosis of lung cancer. International Journal of Latest Trends in Computing, 2, 165-171. doi: 10.1.1.301.6558
  • Lin, P., Li, C., Wang, Z., Li, Y., Jiang, H., Song, W., & Xia, Q. (2020). Three-dimensional memristor circuits as complex neural networks. Nature Electronics, 3, 225-232.
  • Marlen, A., & Dorzhigulov, A. (2018). Fuzzy membership function implementation with memristor. Computer Science, Emerging Technologies, 1-4.
  • Muthuswamy, B. (2010). Implementing memristor based chaotic circuits. International Journal of Bifurcation and Chaos, 20, 1335-1350. doi: 10.1142/S0218127410026514
  • Parlar, I., Almalı M. N., & Cabuker, A. C. (2021). Analysis of wien bridge oscillator designed using BJT and memristor with different window functions. Avrupa Bilim ve Teknoloji Dergisi, 28, 140-143. doi: 10.31590/ejosat.993302
  • Tarkhan, M., & Maymandi-Nejad, M. (2018). Design of a memristor based fuzzy processor. AEU-International Journal of Electronics and Communications, 84, 331-341. doi: 10.1016/j.aeue.2017.10.039
  • Wang, S., Wang, W., Yakopcic, C., Shin, E., Subramanyam, G., & Taha, T. M. (2017). Experimental study of LiNbO3 memristors for use in neuromorphic computing. Microelectronic Engineering, 168, 37-40. doi: 10.1016/j.mee.2016.10.007
  • Wen, S., Xiao, S., Yang, Y., Yan, Z., Zeng, Z., & Huang, T. (2018). Adjusting learning rate of memristor-based multilayer neural networks via fuzzy method. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38, 1084-1094. doi: 10.1109/TCAD.2018.2834436
  • Wen, S., Zeng, Z., Huang T., & Chen, Y. (2013). Fuzzy modeling and synchronization of different memristor-based chaotic circuits. Physics Letters, 377, 34-36. doi: 10.1016/j.physleta.2013.05.046
  • Wlaźlak, E., Marzec, M., Zawal, P., & Szaciłowski, K. (2019). Memristor in a reservoir system experimental evidence for high-level computing and neuromorphic behavior of PbI2. ACS applied materials & interfaces, 11, 17009-17018. doi: 10.1021/acsami.9b01841
  • Yakopcic, C., Taha, T. M., Mountain, D. J., Salter, T., Marinella, M. J., & McLean, M. (2019). Memristor model optimization based on parameter extraction from device characterization data. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39, 1084-1095. doi: 10.1109/TCAD.2019.2912946
  • Yener, Ş. Ç., Mutlu, R., & Kuntman, H. H. (2014). Performance analysis of a memristor-based biquad filter using a dynamic model. Informacije Midem-Journal of Microelectronics Electronic Components and Materials, 44, 109-118.

Estimation of Memristor Hysteresis Curve with Fuzzy Logic Designer

Year 2022, , 64 - 72, 25.04.2022
https://doi.org/10.53433/yyufbed.1070358

Abstract

In this study aimed to optimize the predictable region of the memristor hysteresis curve with the fuzzy logic designer tool by using the linear, doped drift TiO2 model as a memristor emulation circuit. Using this model, the current-voltage (I-V) curve of the memristor characteristic, that is, the hysteresis loop was created. In analog application studies, especially in filter circuits, being able to obtain a hysteresis loop in a certain frequency range leads to significant changes in the quality of the filter. At this point, various trial and error tests were performed for the most suitable parameter points. Here, the optimum hysteresis loop parameters were determined using a fuzzy logic designer. As a result, more practical and more stable results were obtained about the optimum hysteresis loop depending on the current-voltage and frequency information. In this way, users can get more predictable responses by determining the parameters according to the desired purpose and need. Especially in analog and digital electronic applications, they can approach the result more decisively by determining both the time loss and the workable area easily.

References

  • Abdel-Kader, R. F., & Abuelenin, S. M. (2015). Memristor model based on fuzzy window function. IEEE International Conference on Fuzzy Systems (FUZZY-IEEE),1-5. doi: 10.1109/FUZZ-IEEE.2015.7338105
  • Biolek, Z., Biolek, D., & Biolkova, V. (2009). SPICE model of memristor with nonlinear dopant drift. Radio engineering, 18.
  • Chua, L. O., & Kang, S. M. (1976). Memristive devices and systems. Proceedings of the IEEE, 64, 209-223. doi:10.1109/PROC.1976.10092
  • Görgülü, Ö., & Bek, S. Ş. Y. (2017). Analysis of Fuzzy Logic Applications via Matlab.
  • Joglekar, Y. N., & Wolf, S. J. (2009). The elusive memristor: properties of basic electrical circuits. European Journal of physics, 30, 661. doi: 10.1088/0143-0807/30/4/001/meta
  • Kim, H., Sah, M. P., Yang, C., Cho, S., & Chua, L. O. (2012). Memristor emulator for memristor circuit applications. IEEE Transactions on Circuits and Systems I: Regular Papers, 59, 2422-2431. doi: 10.1109/TCSI.2012.2188957
  • Lavanya, K., Durai M. S., & Iyengar, N. C. (2011). Fuzzy rule based inference system for detection and diagnosis of lung cancer. International Journal of Latest Trends in Computing, 2, 165-171. doi: 10.1.1.301.6558
  • Lin, P., Li, C., Wang, Z., Li, Y., Jiang, H., Song, W., & Xia, Q. (2020). Three-dimensional memristor circuits as complex neural networks. Nature Electronics, 3, 225-232.
  • Marlen, A., & Dorzhigulov, A. (2018). Fuzzy membership function implementation with memristor. Computer Science, Emerging Technologies, 1-4.
  • Muthuswamy, B. (2010). Implementing memristor based chaotic circuits. International Journal of Bifurcation and Chaos, 20, 1335-1350. doi: 10.1142/S0218127410026514
  • Parlar, I., Almalı M. N., & Cabuker, A. C. (2021). Analysis of wien bridge oscillator designed using BJT and memristor with different window functions. Avrupa Bilim ve Teknoloji Dergisi, 28, 140-143. doi: 10.31590/ejosat.993302
  • Tarkhan, M., & Maymandi-Nejad, M. (2018). Design of a memristor based fuzzy processor. AEU-International Journal of Electronics and Communications, 84, 331-341. doi: 10.1016/j.aeue.2017.10.039
  • Wang, S., Wang, W., Yakopcic, C., Shin, E., Subramanyam, G., & Taha, T. M. (2017). Experimental study of LiNbO3 memristors for use in neuromorphic computing. Microelectronic Engineering, 168, 37-40. doi: 10.1016/j.mee.2016.10.007
  • Wen, S., Xiao, S., Yang, Y., Yan, Z., Zeng, Z., & Huang, T. (2018). Adjusting learning rate of memristor-based multilayer neural networks via fuzzy method. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38, 1084-1094. doi: 10.1109/TCAD.2018.2834436
  • Wen, S., Zeng, Z., Huang T., & Chen, Y. (2013). Fuzzy modeling and synchronization of different memristor-based chaotic circuits. Physics Letters, 377, 34-36. doi: 10.1016/j.physleta.2013.05.046
  • Wlaźlak, E., Marzec, M., Zawal, P., & Szaciłowski, K. (2019). Memristor in a reservoir system experimental evidence for high-level computing and neuromorphic behavior of PbI2. ACS applied materials & interfaces, 11, 17009-17018. doi: 10.1021/acsami.9b01841
  • Yakopcic, C., Taha, T. M., Mountain, D. J., Salter, T., Marinella, M. J., & McLean, M. (2019). Memristor model optimization based on parameter extraction from device characterization data. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39, 1084-1095. doi: 10.1109/TCAD.2019.2912946
  • Yener, Ş. Ç., Mutlu, R., & Kuntman, H. H. (2014). Performance analysis of a memristor-based biquad filter using a dynamic model. Informacije Midem-Journal of Microelectronics Electronic Components and Materials, 44, 109-118.
There are 18 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

İshak Parlar 0000-0002-3383-8091

Mehmet Nuri Almalı 0000-0003-2763-4452

Ali Can Çabuker 0000-0003-2011-2117

Publication Date April 25, 2022
Submission Date February 8, 2022
Published in Issue Year 2022

Cite

APA Parlar, İ., Almalı, M. N., & Çabuker, A. C. (2022). Estimation of Memristor Hysteresis Curve with Fuzzy Logic Designer. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 64-72. https://doi.org/10.53433/yyufbed.1070358