Abstract
In neuromorphic computing, memristors (or “memory resistors”) have been primarily studied as key elements in artificial synapse implementations, where the memristor provides a variable weight with intrinsic long-term memory capabilities, based on its modifiable resistive-switching characteristics. Here, we demonstrate an efficient methodology for simulating resistive-switching of HfO2 memristors within Synopsys TCAD Sentaurus—a well established, versatile framework for electronic device simulation, visualization and modeling. Kinetic Monte Carlo is used to model the temporal dynamics of filament formation and rupture wherein additional band-to-trap electronic transitions are included to account for polaronic effects due to strong electron-lattice coupling in HfO2. The conductive filament is modeled as oxygen vacancies which behave as electron traps as opposed to ionized donors, consistent with recent experimental data showing p-type conductivity in HfOx films having high oxygen vacancy concentrations and ab-initio calculations showing the increased thermodynamic stability of neutral and charged oxygen vacancies under conditions of electron injection. Pulsed IV characteristics are obtained by inputting the dynamic state of the system—which consists of oxygen ions, unoccupied oxygen vacancies, and occupied oxygen vacancies at various positions—into Synopsis TCAD Sentaurus for quasi-static simulations. This allows direct visualization of filament electrostatics as well as the implementation of a nonlocal, trap-assisted-tunneling model to estimate current-voltage characteristics during switching. The model utilizes effective masses and work functions of the top and bottom electrodes as additional parameters influencing filament dynamics. Together, this approach can be used to provide valuable device- and circuit-level insight, such as forming voltage, resistance levels and success rates of programming operations, as we demonstrate.
Highlights
In recent years, memristor devices have shown great potential for neuromorphic computing due to their resistive-switching dynamics and electrical behavior resembling that of biological synapses (Chua, 1971; Xia and Yang, 2019; Strukov et al, 2008)
Using TCAD Sentaurus (Synopsys, 2019), we demonstrate that the common forming, reset and set characteristics can be successfully reproduced and visualized
We have argued the use of a simple model of filament evolution that makes explicit use of Fermi-Dirac statistics, coupling the rate of defect generation and recombination to electronic transitions associated with conduction and lattice relaxation
Summary
Memristor devices have shown great potential for neuromorphic computing due to their resistive-switching dynamics and electrical behavior resembling that of biological synapses (Chua, 1971; Xia and Yang, 2019; Strukov et al, 2008). Since the oxide thickness tends to be thin (∼ 2–5 nm) (Pi et al, 2019) and the switching speed can be very fast (
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