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The predictive capability of existing physical descriptions of multi-state devices (e.g., oxide memristors, ferroelectrics, antiferroelectric, etc.) cannot be fully leveraged in circuit simulations due to practical limitations regarding the complexity of compact models. We attempt to circumvent this issue by adopting a machine-learning (ML) - based approach to develop a compact model that retains the full physical description of these devices. ML-based modeling approaches have garnered immense interest in recent years and have already been successfully utilized in making models for several novel devices. A known hurdle for ML-based compact modeling is the need for a large amount of experimental data to properly train the model. We propose a method to simulate additional data by duplicating the data and adding Gaussian Noise to the duplicates. We propose a generalized framework to - (i) facilitate efficient training of ML-based device models, (ii) conduct seamless conversion to a Verilog-A model, and (iii) interface with industry-standard circuit simulators (HSPICE, SPECTRE, etc.). We demonstrate the capabilities of our framework using the hafnium oxide (HfOx) memristor as a test device. As the source of the training data, we use a physical model that unifies detailed atomic-level descriptions with self-consistent evaluation of electronic transport. In addition, we test our model with experimental data for multiple memristor samples and repeated cycles of the same sample. Our ML-based framework prepares a circuit-compatible compact model to facilitate system-level simulations. With our model, we achieve a root mean squared error (RMSE) of 0.000863 and an R2 of 0.977371 on our testing data.

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.