Structural and Parametric Identification of Knowm Memristors
- The novel identification method is presented as a generalized process for a wide range of memristive elements.
- The proposed memristor model outperforms the existing ones in representing the switching threshold as a function of the state variables vector, making it possible to account for snapforward or snapback effects, frequency properties, and switching variability.
- The process and results of the parametric identification for the proposed memristor model are presented.
2. Materials and Methods
2.1. Procedure for Identification of Memristive Elements
2.2. Knowm Memristive Devices
2.3. Experimental Setup
2.4. Modeling Criteria
- Correspondence of the model’s I-V curve and the switching dynamics in the time domain to the experimental data of real devices. Therefore, in Figure 3a,b, the SET transition process of the device from HRS to LRS demonstrates a sharp current increase with a shift of the voltage switching boundary (snapback) at the initial stage <1>, in case (c) the SET transition looks smooth throughout the entire section. The reverse RESET process, which switches the device to HRS, can either be instantaneous (a), snapforward effect, or significantly slower (b) and (c). The symmetry of the I-V curve relative to the diagonal of the II and IV quarters is often violated. This can also be visualized in the time domain when AC voltage is applied. In addition, there is a visible curvature of the <1> section due to the metal-semiconductor/insulator barrier between the electrodes and the inner layers of the devices.
- Nonlinearity of the switching function. The origin of this nonlinearity in memristive devices based on redox reactions is explained by the nonlinear movement of ion vacancies or defects, accelerated by Joule heating. This property is characterized in that the resistance switching time of the SET and RESET processes decreases by orders of magnitude if the applied voltage pulse increases only several times. Thus, this criterion tests the model for a nonlinear dependence of the switching time on the input voltage.
- Suitability for modeling the complementary serial connection of two elements. One of the distinguishing features of such a connection is the presence of a common LRS when AC voltage is applied. This criterion serves as a check for the consistency of the memristive device model.
- Ability to set several states of resistance. The criterion is to identify more than two states of resistance of the memristive device between the LRS and HRS, providing multi-bit data storage.
- Dependence of SET (or RESET) switching from the current state of the resistance. According to this criterion, the voltage required to set the device to a lower resistance state should depend on the high resistance value in the current cycle and vice versa. Thus, the switching kinetics should be power-dependent.
- Reliable simulation of the memory fading effect. This dynamical phenomenon is well known in the theory of nonlinear systems. With suitable periodic exposure, the previous “history” of the memristive device is gradually erased.
- Compact representation of a continuous mathematical model, which determines the suitability of using its discrete version in the processes of large-system simulation, including neural networks, as well as digital hardware emulators of memristive circuits.
2.5. Candidate Memristor Models
2.5.1. Mean Metastable Switch Memristor Model
2.5.2. Generalized Mean Metastable Switch Memristor Model
3.1. Criterial Analysis of Candidate Models
3.2. The Modification of Memristor Model
3.3. Parametric Identification
Data Availability Statement
Conflicts of Interest
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Ostrovskii, V.; Fedoseev, P.; Bobrova, Y.; Butusov, D. Structural and Parametric Identification of Knowm Memristors. Nanomaterials 2022, 12, 63. https://doi.org/10.3390/nano12010063
Ostrovskii V, Fedoseev P, Bobrova Y, Butusov D. Structural and Parametric Identification of Knowm Memristors. Nanomaterials. 2022; 12(1):63. https://doi.org/10.3390/nano12010063Chicago/Turabian Style
Ostrovskii, Valerii, Petr Fedoseev, Yulia Bobrova, and Denis Butusov. 2022. "Structural and Parametric Identification of Knowm Memristors" Nanomaterials 12, no. 1: 63. https://doi.org/10.3390/nano12010063