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Journal articles on the topic 'Memristance'

Author: Grafiati

Published: 14 December 2024

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1

Yang, Le, and Zhixia Ding. "A Memristor-Based High-Resolution A/D Converter." Electronics 11, no. 9 (May 3, 2022): 1470. http://dx.doi.org/10.3390/electronics11091470.

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Based on the voltage threshold adaptive memristor (VTEAM) model, this paper proposes a circuit design of a memristor-based A/D converter, which can achieve high-resolution conversion by simple configuration. For this A/D converter, there are the input voltage stage and the reference voltage stage in one conversion. According to the memristance change in the two stages, the input analog voltage is converted as the corresponding digital value. In the input voltage stage, the memristance increases from the initial memristance. Meanwhile, the counter rises its value from zero to the maximum. Next, the memristance returns to the initial memristance in the reference voltage stage. At the same time, the counting value starts to increase from zero again. Then, the input analog voltage is mapped to the eventual counting value of the reference voltage stage. The simulations of the memristor-based A/D converter demonstrate that it has good conversion performance. The proposed memristor-based A/D converter not only has more brilliant performance than the CMOS A/D converter, but also has the advantages over existing memristor-based A/D converters of anti-interference ability and high resolution.

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2

Ukil, Abhisek. "Memristance View of Piezoelectricity." IEEE Sensors Journal 11, no. 10 (October 2011): 2514–17. http://dx.doi.org/10.1109/jsen.2011.2114878.

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3

Martinsen, Ø. G., S. Grimnes, C. A. Lütken, and G. K. Johnsen. "Memristance in human skin." Journal of Physics: Conference Series 224 (April 1, 2010): 012071. http://dx.doi.org/10.1088/1742-6596/224/1/012071.

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4

Cam, Zehra Gulru, and Herman Sedef. "A New Floating Memristance Simulator Circuit Based on Second Generation Current Conveyor." Journal of Circuits, Systems and Computers 26, no. 02 (November 3, 2016): 1750029. http://dx.doi.org/10.1142/s0218126617500293.

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In this paper, a new floating analog memristance simulator circuit based on second generation current conveyors and passive elements is proposed. Theoretical derivations are presented which decribe the circuit characteristics. The hardware of proposed simulator circuit is built using commercially available components. Theoretical derivations are validated with PSPICE simulation and experimental results. Performance of circuit was tested with simple example circuits. All results show that proposed simulator circuit provides frequency dependent pinched hysteresis loop and nonvolatility features. Exciting frequency, minimum and maximum memristance values and memristance range can be adjustable with simple passive element values. Simulator circuit has a frequency range of 1[Formula: see text]Hz to 40[Formula: see text]kHz.

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5

Yu, Bo, Yifei Pu, Qiuyan He, and Xiao Yuan. "Principle and Application of Frequency-Domain Characteristic Analysis of Fractional-Order Memristor." Micromachines 13, no. 9 (September 12, 2022): 1512. http://dx.doi.org/10.3390/mi13091512.

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Scaling fractional-order memristor circuit is important for realizing a fractional-order memristor. However, the effective operating-frequency range, operation order, and fractional-order memristance of the scaling fractional-order memristor circuit have not been studied thoroughly; that is, the fractional-order memristance in the effective operating-frequency range has not been calculated quantitatively. The fractional-order memristance is a similar and equally important concept as memristance, memcapacitance, and meminductance. In this paper, the frequency-domain characteristic-analysis principle of the fractional-order memristor is proposed based on the order- and F-frequency characteristic functions. The reasons for selecting the order- and F-frequency characteristic functions are explained. Subsequently, the correctness of the frequency-domain characteristic analysis using the order- and F-frequency characteristic functions is verified from multiple perspectives. Finally, the principle of the frequency-domain characteristic analysis is applied to the recently realized chain-scaling fractional-order memristor circuit. The results of this study indicate that the principle of the frequency-domain characteristic analysis of the fractional-order memristor can successfully calculate the fractional-order memristance of the chain-scaling fractional-order memristor circuit. The proposed principle of frequency-domain characteristic analysis can also be applied to mem-elements, such as memristors, memcapacitors, and meminductors. The main contribution of this study is the principle of the frequency-domain characteristic analysis of the fractional-order memristor based on the order- and F-frequency characteristic functions.

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6

MUTLU, Reşat, and Ertuğrul KARAKULAK. "A methodology for memristance calculation." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 22 (2014): 121–31. http://dx.doi.org/10.3906/elk-1205-16.

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7

Banchuin, Rawid. "On the Memristances, Parameters, and Analysis of the Fractional Order Memristor." Active and Passive Electronic Components 2018 (November 1, 2018): 1–14. http://dx.doi.org/10.1155/2018/3408480.

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In this work, the analytical expressions of memristances, related parameters, and time domain behavioral analysis of the fractional order memristor have been proposed. Both DC with arbitrary delay and many AC waveforms including arbitrary phase sinusoidal and cosinusoidal waveform along with arbitrary periodic waveform have been taken into account. Unlike the previous works, the formerly ignored dimensional consistency has been taken into account and the analytical modelling of the boundary effect has been performed. Moreover, both transient and asymptotic behaviors of the fractional order memristor excited by AC waveform have been distinguished and analyzed. The effect of phase of AC waveform has also been studied. The influence of the fractional order to the areas of voltage-current hysteresis loop and memristance-current lissajous curve has also been clearly discussed and the usage of fractional order memristor in the memristor based circuit has also been demonstrated.

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8

Le, Minh, Thi Kim Hang Pham, and Son Ngoc Truong. "Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition." Micromachines 12, no. 6 (June 13, 2021): 690. http://dx.doi.org/10.3390/mi12060690.

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We performed a comparative study on the Gaussian noise and memristance variation tolerance of three crossbar architectures, namely the complementary crossbar architecture, the twin crossbar architecture, and the single crossbar architecture, for neuromorphic image recognition and conducted an experiment to determine the performance of the single crossbar architecture for simple pattern recognition. Ten grayscale images with the size of 32 × 32 pixels were used for testing and comparing the recognition rates of the three architectures. The recognition rates of the three memristor crossbar architectures were compared to each other when the noise level of images was varied from −10 to 4 dB and the percentage of memristance variation was varied from 0% to 40%. The simulation results showed that the single crossbar architecture had the best Gaussian noise input and memristance variation tolerance in terms of recognition rate. At the signal-to-noise ratio of −10 dB, the single crossbar architecture produced a recognition rate of 91%, which was 2% and 87% higher than those of the twin crossbar architecture and the complementary crossbar architecture, respectively. When the memristance variation percentage reached 40%, the single crossbar architecture had a recognition rate as high as 67.8%, which was 1.8% and 9.8% higher than the recognition rates of the twin crossbar architecture and the complementary crossbar architecture, respectively. Finally, we carried out an experiment to determine the performance of the single crossbar architecture with a fabricated 3 × 3 memristor crossbar based on carbon fiber and aluminum film. The experiment proved successful implementation of pattern recognition with the single crossbar architecture.

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9

Liu, Xiaoxin, Lanqing Zou, Chenyang Huang, Na Bai, Kanhao Xue, Huajun Sun, and Xiangshui Miao. "Analog Memristor-Based Dynamic Programmable Analog Filter." Journal of Physics: Conference Series 2356, no. 1 (October 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2356/1/012008.

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The memristor study now generally exhibit threshold voltage characteristics. The memristance changes when the voltage across the memristor is greater than the threshold voltage. Otherwise, the memristance is almost constant. Based on this feature, we design a general-purpose memristor programmable circuit that is simple to operate. In the field of communication and signal processing, programmable analog filters are required, and memristors with multi-valued characteristic are suitable as programmable impedance elements for such circuits. Through simulation and physics experiments, we demonstrate a memristor-based programmable low-pass filter using the designed programmable circuit to realize the dynamic adjustment of circuit parameters.

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10

Bunnam, Thanasin, Ahmed Soltan, Danil Sokolov, Oleg Maevsky, and Alex Yakovlev. "Toward Designing Thermally-Aware Memristance Decoder." IEEE Transactions on Circuits and Systems I: Regular Papers 66, no. 11 (November 2019): 4337–47. http://dx.doi.org/10.1109/tcsi.2019.2925021.

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11

Du, Nan, Yao Shuai, Wenbo Luo, Christian Mayr, René Schüffny, Oliver G. Schmidt, and Heidemarie Schmidt. "Practical guide for validated memristance measurements." Review of Scientific Instruments 84, no. 2 (February 2013): 023903. http://dx.doi.org/10.1063/1.4775718.

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12

Tanaka, H., Y. Tadokoro, and H. Iizuka. "Memristance enhancement by external voltage source." Electronics Letters 49, no. 23 (November 2013): 1446–48. http://dx.doi.org/10.1049/el.2013.2311.

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13

Koner, Subhadeep, Joseph S. Najem, Md Sakib Hasan, and Stephen A. Sarles. "Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes." Nanoscale 11, no. 40 (2019): 18640–52. http://dx.doi.org/10.1039/c9nr07288h.

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An artificial electrical synapse that mimics the structure, transport properties, and plasticity of biological electrical synapses exhibits voltage-controlled memristance by exploiting reconfigurable membrane geometry.

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14

Li, Y., Y. P. Zhong, J. J. Zhang, X. H. Xu, Q. Wang, L. Xu, H. J. Sun, and X. S. Miao. "Intrinsic memristance mechanism of crystalline stoichiometric Ge2Sb2Te5." Applied Physics Letters 103, no. 4 (July 22, 2013): 043501. http://dx.doi.org/10.1063/1.4816283.

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15

Taşkıran, Zehra Gülru Çam, and Murat Taşkıran. "Second Generation Current Conveyor Based Floating Fractional Order Memristance Simulator and a New Dynamical System." Cybernetics and Information Technologies 20, no. 5 (December 1, 2020): 68–80. http://dx.doi.org/10.2478/cait-2020-0041.

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AbstractIn recent years, due to its non-volatile memory, non-locality, and weak singularity features, fractional calculations have begun to take place frequently in artificial neural network implementations and learning algorithms. Therefore, there is a need for circuit element implementations providing fractional function behaviors for the physical realization of these neural networks. In this study, a previously defined integer order memristor element equation is changed and a fractional order memristor is given in a similar structure. By using the obtained mathematical equation, frequency-dependent pinched hysteresis loops are obtained. A memristance simulator circuit that provides the proposed mathematical relationship is proposed. Spice simulations of the circuit are run and it is seen that they are in good agreement with the theory. Also, the non-volatility feature has been demonstrated with Spice simulations. The proposed circuit can be realized by using the integrated circuit elements available on the market. With a small connection change, the proposed structure can be used to produce both positive and negative memristance values.

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16

Budhathoki, Ram Kaji, Maheshwar P. D. Sah, Changju Yang, Hyongsuk Kim, and Leon Chua. "Transient Behaviors of Multiple Memristor Circuits Based on Flux Charge Relationship." International Journal of Bifurcation and Chaos 24, no. 02 (February 2014): 1430006. http://dx.doi.org/10.1142/s0218127414300067.

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Memristor, a new electrical element, can have various configurations of multiple memristors, including serial and parallel connections like previous elements R, L and C. When input voltage/current is supplied to a circuit with multiple memristors, the composite behavior of the memristor circuit exhibits transient states before it enters a steady state. During the transient state period, the behavior is very complex and not predictable due to each memristor's different action depending upon its connection polarity and initial state. In this paper, the transient characteristics of a composite memristor are analyzed via the relationships of charge, flux and memristance of each memristor. Also, the behavior of an individual memristor is formulated mathematically and a general computation method of composite memristance for multiple-memristor circuits of diverse configurations is proposed. Various simulations have also been performed to verify the effectiveness of the proposed method for differently configured memristor circuits, in terms of polarities and initial states.

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17

Khadar Basha, N., and Dr T Ramashri. "Operating conditions analysis of memristor model." International Journal of Engineering & Technology 7, no. 4 (September 17, 2018): 2291. http://dx.doi.org/10.14419/ijet.v7i4.9684.

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The two terminal, fourth basic circuit element, memristor acts as nonlinear resistor with built-in memory functionality. Memristor has many advantages like non-volatile, no leakage current, Even when the power supply turn off, it retains its memory and typically apparent only at small scale. It shows significant effect in digital circuit application because it stores logic values without power consumption and logic values are measured based on the memristance value. Memristor is a class of non-volatile memory storage and is suitable for nanoscale memory applications. It is considered one of the most promising technology to implement memory and logic operations in a single cell. In this technology stored information is calculated as a low resistive state (LRS) and high resistive state (HRS). A detailed operating conditions of tunneling modulation model of memristor is studied and analyzed the operating frequency and voltage ranges in this paper. Switching behavior is measured based on the transition time of memristance change from one state to another state at different working frequencies.

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18

Ramadoss, Janarthanan, Othman Abdullah Almatroud, Shaher Momani, Viet-Thanh Pham, and Vo Phu Thoai. "Discrete Memristance and Nonlinear Term for Designing Memristive Maps." Symmetry 14, no. 10 (October 11, 2022): 2110. http://dx.doi.org/10.3390/sym14102110.

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Chaotic maps have simple structures but can display complex behavior. In this paper, we apply discrete memristance and a nonlinear term in order to design new memristive maps. A general model for constructing memristive maps has been presented, in which a memristor is connected in serial with a nonlinear term. By using this general model, different memristive maps have been built. Such memristive maps process special fixed points (infinite and without fixed point). A typical memristive map has been studied as an example via fixed points, bifurcation diagram, symmetry, and coexisting iterative plots.

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19

Tsoukalas, Dimitris, and Emanuele Verrelli. "Inorganic Nanoparticles for either Charge Storage or Memristance Modulation." Advances in Science and Technology 77 (September 2012): 196–204. http://dx.doi.org/10.4028/www.scientific.net/ast.77.196.

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We present prototype memory devices using metallic and metal oxide nanoparticles obtained by a physical deposition technique. The two memory device examples demonstrated concern the use of platinum nanoparticles for flash-type memories and the use of titanium oxide nanoparticles for resistive memories. Both approaches give interesting device memory properties with resistive memories being still in an early exploratory phase.

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20

Biolek, Zdeněk, Dalibor Biolek, Viera Biolková, Zdeněk Kolka, Alon Ascoli, and Ronald Tetzlaff. "Analysis of memristors with nonlinear memristance versus state maps." International Journal of Circuit Theory and Applications 45, no. 11 (January 25, 2017): 1814–32. http://dx.doi.org/10.1002/cta.2314.

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21

Khan, Samiur Rahman, AlaaDdin Al-Shidaifat, and Hanjung Song. "Efficient Memristive Circuit Design of Neural Network-Based Associative Memory for Pavlovian Conditional Reflex." Micromachines 13, no. 10 (October 15, 2022): 1744. http://dx.doi.org/10.3390/mi13101744.

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The brain’s learning and adaptation processes heavily rely on the concept of associative memory. One of the most basic associative learning processes is classical conditioning. This work presents a memristive neural network-based associative memory system. The system can emulate Pavlovian conditioning principles including acquisition, extension, generalization, differentiation, and spontaneous recovery that have not been considered in most of the previous counterparts. The proposed circuit can emulate these principles thanks to the resistance-changing characteristics of the memristor. Generalization has been achieved by providing both unconditional and neutral stimuli to the network to reduce the memristance of the memristor. Differentiation has been attained by employing unconditional and conditional stimuli in a training scheme to obtain a certain memristance that causes the network to respond differently to both stimuli. A revival of an exterminated stimuli is also done by increasing the synaptic weight of the system. Compared to previous designs, the proposed memristive circuit can implement all the functions of conditional reflex. Our rigorous simulations demonstrated that the proposed memristive system can condition neutral stimuli, show generalization between similar stimuli, distinguish dissimilarities between the generalized stimuli, and recover faded stimuli.

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22

Cam Taskiran, Zehra Gulru, Murat Taşkıran, Mehmet Kıllıoğlu, Nihan Kahraman, and Herman Sedef. "A novel memristive true random number generator design." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 6 (October 24, 2019): 1931–47. http://dx.doi.org/10.1108/compel-11-2018-0463.

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Purpose In this work, a true random number generator is designed by sampling the double-scroll analog continuous-time chaotic circuit signals. Methodology A Chua circuit based on memristance simulator is designed to obtain a non-linear term for a chaotic dynamic system. It is implemented on the board by using commercially available integrated circuits and passive elements. A low precision ADC which is commonly found in the market is used to sample the chaotic signals. The mathematical analysis of the chaotic circuit is verified by experimental results. Originality It is aimed to be one of the pioneering studies (including low precision ADC) in the literature on the implementation of memristive chaotic random number generators. Findings Two new methods are proposed for post-processing and creating random bit array using XOR operator and J-K flip flop. The bit stream obtained by a full-hardware implementation successfully passed the NIST-800-22 test. In this respect, the availability of the memristance simulator circuit, memristive chaotic double-scroll attractor, proposed random bit algorithm and the randomness of the memristive analog continuous-time chaotic true number generator were also verified.

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23

Maundy, B. J., A. S. Elwakil, and C. Psychalinos. "CMOS Realization of All-Positive Pinched Hysteresis Loops." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/7863095.

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Two novel nonlinear circuits that exhibit an all-positive pinched hysteresis loop are proposed. These circuits employ two NMOS transistors, one of which operates in its triode region, in addition to two first-order filter sections. We show the equivalency to a charge-controlled resistance (memristance) in a decremental state via detailed analysis. Simulation and experimental results verify the proposed theory.

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24

Tanaka, Hiroya, Yukihiro Tadokoro, and Hideo Iizuka. "Optimal condition of memristance enhancement circuit using external voltage source." AIP Advances 4, no. 5 (May 2014): 057117. http://dx.doi.org/10.1063/1.4879287.

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25

Junsangsri, Pilin, and Fabrizio Lombardi. "Design of a Hybrid Memory Cell Using Memristance and Ambipolarity." IEEE Transactions on Nanotechnology 12, no. 1 (January 2013): 71–80. http://dx.doi.org/10.1109/tnano.2012.2229715.

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26

Alialy, Sahar, Koorosh Esteki, Mauro S. Ferreira, John J. Boland, and Claudia Gomes da Rocha. "Nonlinear ion drift-diffusion memristance description of TiO2 RRAM devices." Nanoscale Advances 2, no. 6 (2020): 2514–24. http://dx.doi.org/10.1039/d0na00195c.

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27

Torres-Costa, Vicente, Ermei Mäkilä, Sari Granroth, Edwin Kukk, and Jarno Salonen. "Synaptic and Fast Switching Memristance in Porous Silicon-Based Structures." Nanomaterials 9, no. 6 (May 31, 2019): 825. http://dx.doi.org/10.3390/nano9060825.

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Memristors are two terminal electronic components whose conductance depends on the amount of charge that has flown across them over time. This dependence can be gradual, such as in synaptic memristors, or abrupt, as in resistive switching memristors. Either of these memory effects are very promising for the development of a whole new generation of electronic devices. For the successful implementation of practical memristors, however, the development of low cost industry compatible memristive materials is required. Here the memristive properties of differently processed porous silicon structures are presented, which are suitable for different applications. Electrical characterization and SPICE simulations show that laser-carbonized porous silicon shows a strong synaptic memristive behavior influenced by defect diffusion, while wet-oxidized porous silicon has strong resistance switching properties, with switching ratios over 8000. Results show that practical memristors of either type can be achieved with porous silicon whose memristive properties can be adjusted by the proper material processing. Thus, porous silicon may play an important role for the successful realization of practical memristorics with cost-effective materials and processes.

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Adhikari, Shyam Prasad, Hyongsuk Kim, Bai-Sun Kong, and Leon O. Chua. "Memristance drift avoidance with charge bouncing for memristor-based nonvolatile memories." Journal of the Korean Physical Society 61, no. 9 (November 2012): 1418–21. http://dx.doi.org/10.3938/jkps.61.1418.

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29

Liu, Hai-Jun, Zhi-Wei Li, Hong-Qi Yu, Zhao-Lin Sun, and Hong-Shan Nie. "Memristance controlling approach based on modification of linear M — q curve." Chinese Physics B 23, no. 11 (November 2014): 118402. http://dx.doi.org/10.1088/1674-1056/23/11/118402.

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30

Zhang, Kun, Yan-ling Cao, Yue-wen Fang, Qiang Li, Jie Zhang, Chun-gang Duan, Shi-shen Yan, et al. "Electrical control of memristance and magnetoresistance in oxide magnetic tunnel junctions." Nanoscale 7, no. 14 (2015): 6334–39. http://dx.doi.org/10.1039/c5nr00522a.

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31

Juhas, Anamarija, and Stanisa Dautovic. "Computation of Pinched Hysteresis Loop Area From Memristance-vs-State Map." IEEE Transactions on Circuits and Systems II: Express Briefs 66, no. 4 (April 2019): 677–81. http://dx.doi.org/10.1109/tcsii.2018.2868384.

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32

Yang, Le, Zhigang Zeng, and Shiping Wen. "A full-function Pavlov associative memory implementation with memristance changing circuit." Neurocomputing 272 (January 2018): 513–19. http://dx.doi.org/10.1016/j.neucom.2017.07.020.

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Cam Taskiran, Zehra Gulru, Umut Engin Ayten, and Herman Sedef. "Dual-Output Operational Transconductance Amplifier-Based Electronically Controllable Memristance Simulator Circuit." Circuits, Systems, and Signal Processing 38, no. 1 (May 25, 2018): 26–40. http://dx.doi.org/10.1007/s00034-018-0856-y.

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Román Acevedo, W., C. A. M. van den Bosch, M. H. Aguirre, C. Acha, A. Cavallaro, C. Ferreyra, M. J. Sánchez, L. Patrone, A. Aguadero, and D. Rubi. "Large memcapacitance and memristance at Nb:SrTiO3/La0.5Sr0.5Mn0.5Co0.5O3-δ topotactic redox interface." Applied Physics Letters 116, no. 6 (February 10, 2020): 063502. http://dx.doi.org/10.1063/1.5131854.

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Shuai, Yao, Nan Du, Xin Ou, Wenbo Luo, Shengqiang Zhou, Oliver G. Schmidt, and Heidemarie Schmidt. "Improved retention of nonvolatile bipolar BiFeO3resistive memories validated by memristance measurements." physica status solidi (c) 10, no. 4 (March 13, 2013): 636–39. http://dx.doi.org/10.1002/pssc.201200881.

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Liu, Ruxin, Ruixin Dong, Xunling Yan, Shuai Yuan, Dong Zhang, Bing Yang, and Xia Xiao. "Two-parameter multi-state memory device based on memristance and memcapacitance characteristics." Applied Physics Express 11, no. 11 (October 26, 2018): 114103. http://dx.doi.org/10.7567/apex.11.114103.

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37

Howard, Sebastian A., Christopher N. Singh, Galo J. Paez, Matthew J. Wahila, Linda W. Wangoh, Shawn Sallis, Keith Tirpak, et al. "Direct observation of delithiation as the origin of analog memristance in LixNbO2." APL Materials 7, no. 7 (July 2019): 071103. http://dx.doi.org/10.1063/1.5108525.

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Pratyusha, Nune, and Santanu Mandal*. "Realization of Memory Effect on Hysteresis Lobe Area of the TiO2 Based HP Memristor." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (October 30, 2019): 321–24. http://dx.doi.org/10.35940/ijitee.l1982.1081219.

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In this paper, the memory effect on the hysteresis lobe area of TiO2 based memristive model is studied. The Green’s theorem is used to derive the novel general formula for the area of hysteresis lobe of HP memristor model. Further the memory and boundary values are derived mathematically, where the memory needs to be stable. It is analyzed that in the initial state this nanoscale non-volatile memristor retains its memory. The relation of memory with the lobe area and memristance are also established respectively. The analytical results mentioned above are demonstrated by numerical simulations and graphical representations.

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Carrasco-Aguilar, Miguel Angel, Carlos Sanchez-López, and Francisco Epimenio Morales-López. "Current-controlled grounded memristor emulator circuit based on analog multiplier." Journal of Applied Research and Technology 20, no. 3 (July 1, 2022): 347–54. http://dx.doi.org/10.22201/icat.24486736e.2022.20.3.932.

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This paper proposes a current-controlled grounded memristor emulator circuit based on single four-quadrant analog multiplier, a resistor and a capacitor. The behavioral model of the proposed emulator circuit is analyzed, highlighting its characteristics. Experimental results are given to investigate its ability for different operating frequencies and they are in accordance with theoretical analysis and simulation results. By using a divider circuit, the memristance variation in the time domain is obtained. It is observed that the pinched hysteresis loop at high frequency loses symmetry due in part to the shift that introduces a multiplication of sinusoidal signals.

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Li, Guodong, Huiyan Zhong, Wenxia Xu, and Xiangliang Xu. "Two Modified Chaotic Maps Based on Discrete Memristor Model." Symmetry 14, no. 4 (April 12, 2022): 800. http://dx.doi.org/10.3390/sym14040800.

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The discrete memristor has aroused increasing interest. In this paper, two discrete memristors with cosine with amplitude memristance are designed based on the discrete memristor model. The Simulink models of the two discrete memristors are built to verify that they meet the definition of the memristor. To improve the dynamic of a classic chaotic map, the discrete memristors are introduced into two chaotic maps: a Logistic map and a Hénon Map. Through the trajectory analysis, Lyapunov exponent, bifurcation diagram, and complexity analysis, it is shown that discrete memristors can indeed make the dynamical behaviors of chaotic maps richer and more complex.

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Rodriguez, N., D. Maldonado, F. J. Romero, F. J. Alonso, A. M. Aguilera, A. Godoy, F. Jimenez-Molinos, F. G. Ruiz, and J. B. Roldan. "Resistive Switching and Charge Transport in Laser-Fabricated Graphene Oxide Memristors: A Time Series and Quantum Point Contact Modeling Approach." Materials 12, no. 22 (November 13, 2019): 3734. http://dx.doi.org/10.3390/ma12223734.

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This work investigates the sources of resistive switching (RS) in recently reported laser-fabricated graphene oxide memristors by means of two numerical analysis tools linked to the Time Series Statistical Analysis and the use of the Quantum Point Contact Conduction model. The application of both numerical procedures points to the existence of a filament connecting the electrodes that may be interrupted at a precise point within the conductive path, resulting in resistive switching phenomena. These results support the existing model attributing the memristance of laser-fabricated graphene oxide memristors to the modification of a conductive path stoichiometry inside the graphene oxide.

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Markovic, Ivo, Milka Potrebic, Dejan Tosic, and Zlata Cvetkovic. "Comparison of memristor models for microwave circuit simulations in time and frequency domain." Facta universitatis - series: Electronics and Energetics 32, no. 1 (2019): 65–74. http://dx.doi.org/10.2298/fuee1901065m.

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As reported in the open literature, there are many memristor models for the circuit-level simulations. Some of them are not particularly suitable for microwave circuit simulations. At RF/microwave frequencies, the memristor dynamics become an important issue for the transition process. In this paper we present a number of different SPICE memristor model groups. Each group is explained using representative models, which are analysed and compared from the microwave circuit analysis viewpoint. We consider the model behaviour at RF/microwave frequencies and the memristance setting issues. Results are compared and the best models are recommended.

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Budhathoki, Ram Kaji. "Comparative Analysis of Memristor based Synaptic Circuits for Neuromorphic Architectures." SCITECH Nepal 13, no. 1 (September 30, 2018): 32–39. http://dx.doi.org/10.3126/scitech.v13i1.23499.

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Memristor is one of the fundamental electrical elements, which has recently been successfully built. Memristor is being used extensively as synapses in neuromorphic applications. A common method for developing memristor based synaptic circuits is to store synaptic weight values within memristors as resistance values. It requires use of the continuous resistance (memristance) range available in the memristors to store the weights. In this paper, we study how different compact synaptic circuits implemented using Ti02 memristors to perform zero, negative, and positive synaptic weightings. Results of different memristor based circuits, based on pulsed input signals, are compared, analyzed and presented using Ti02 memristors via simulations.

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Mladenov, Valeri, and Stoyan Kirilov. "ANALYSIS OF AN ANTI-PARALLEL MEMRISTOR CIRCUIT." Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska 8, no. 2 (May 30, 2018): 9–14. http://dx.doi.org/10.5604/01.3001.0012.0696.

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The basic purpose of the present paper is to propose an extended investigation and computer analysis of an anti-parallel memristor circuit with two equivalent memristor elements with different initial values of the state variables using a modified Boundary Condition Memristor (BCM) Model and the finite differences method. The memristor circuit is investigated for sinusoidal supply current at different magnitudes – for soft-switching and hard-switching modes, respectively. The influence of the initial values of the state variables on the circuit’s behaviour is presented as well. The equivalent i-v and memristance-flux and the other important relationshipsof the memristor circuit are also analyzed.

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WANG, LIDAN, EMMANUEL DRAKAKIS, SHUKAI DUAN, PENGFEI HE, and XIAOFENG LIAO. "MEMRISTOR MODEL AND ITS APPLICATION FOR CHAOS GENERATION." International Journal of Bifurcation and Chaos 22, no. 08 (August 2012): 1250205. http://dx.doi.org/10.1142/s0218127412502057.

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This paper contributes to the understanding of memristor operation and its possible application fields through: (a) derivation of a complete mathematical model for the HP memristor which takes into consideration the inter-dependence between memristance, charge and flux along with the boundary and initial conditions of operation; (b) an introduction of detailed charge- and flux-controlled SPICE memristor models realizing the proposed mathematical memristor model; (c) The incorporation of the memristor model in the SPICE realization of a third-order chaotic system where a single HP memristor acts as the nonlinear part of the system. Simulation results are provided to validate the mathematical model and the synthesis and operation of the third-order chaotic system.

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MARINCA, Bogdan, and Vasile MARINCA. "Analytical method for nonlinear memristive systems." Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science 24, no. 2 (June 28, 2023): 159–65. http://dx.doi.org/10.59277/pra-ser.a.24.2.08.

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This work is devoted to providing an approximate analytical method to analyze memristor devices. One of basic property of the memristor is pinched hysteresis, considered to be a signature of the existence of memristance. The presence of hysteresis defines the material implementation of its memristive effects. This it its fundamental property, which looks more like a nonlinear anomaly. The memristor technology offers lower heat generation as it utilizes less energy. Optimal auxiliary functions method (OAFM) is implemented to find an approximate solution for the state variable in memristor with a very high accuracy. The presence of the auxiliary functions and some optimal convergence-control parameters Ci assure a fast convergence of the solutions.

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Maier, P., F. Hartmann, J. Gabel, M. Frank, S. Kuhn, P. Scheiderer, B. Leikert, et al. "Gate-tunable, normally-on to normally-off memristance transition in patterned LaAlO3/SrTiO3 interfaces." Applied Physics Letters 110, no. 9 (February 27, 2017): 093506. http://dx.doi.org/10.1063/1.4977834.

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Pickett, Matthew D., Julien Borghetti, J. Joshua Yang, Gilberto Medeiros-Ribeiro, and R. Stanley Williams. "Coexistence of Memristance and Negative Differential Resistance in a Nanoscale Metal-Oxide-Metal System." Advanced Materials 23, no. 15 (February 22, 2011): 1730–33. http://dx.doi.org/10.1002/adma.201004497.

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Yavuz, Kutluhan Kürşad, Ertuğrul Karakulak, and Reşat Mutlu. "Memristor-based series voltage regulators." Journal of Electrical Engineering 70, no. 6 (December 1, 2019): 465–72. http://dx.doi.org/10.2478/jee-2019-0079.

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Abstract Series linear voltage regulators are integrated circuits commonly used to make adjustable voltage sources. When used with potentiometers, these regulators are able to give adjustable voltage at the output. Memristor is a new nonlinear circuit element which came out in the last decade. It is able to provide electronically adjustable resistance. If a memristor is fed with a voltage over the threshold and it is not under saturation, its resistance also called memristance is dependent on the integration of its current, also called memristor charge. Memristor shows promise for different types and lots of digital and analog applications. In this paper, memristor-based series voltage regulator topologies are suggested and they are examined using parameters and simulations. Some design criteria have been given for the memristor-based series voltage regulators.

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Temple, Rowan C., Mark C. Rosamond, Jamie R. Massey, Trevor P. Almeida, Edmund H. Linfield, Damien McGrouther, Stephen McVitie, Thomas A. Moore, and Christopher H. Marrows. "Phase domain boundary motion and memristance in gradient-doped FeRh nanopillars induced by spin injection." Applied Physics Letters 118, no. 12 (March 22, 2021): 122403. http://dx.doi.org/10.1063/5.0038950.

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