Physics-based modeling approaches of resistive switching devices for memory and in-memory computing applications

Area of Science:

• Materials Science and Engineering
• Electrical Engineering
• Computer Science

Background:

• Emerging technologies like IoT, Big Data, and deep learning require advanced memory and computation beyond traditional CMOS.
• Resistive switching memory (RRAM) offers significant potential for storage, memory devices, and in-memory computing.
• A gap exists in industrial tools for predicting RRAM behavior and optimizing material/stack properties.

Purpose of the Study:

• To provide an overview of modeling approaches for RRAM simulation.
• To discuss the adaptation of modeling schemes to different RRAM concepts (filamentary and interface switching).
• To demonstrate the application of compact modeling in simulating RRAM circuits for computing.

Main Methods:

• Review of Technology Computer-Aided Design (TCAD) and high-level compact models for RRAM circuit simulations.
• Discussion of Finite Element Method (FEM) modeling, Kinetic Monte Carlo (KMC) models, and physics-based analytical models.
• Analysis of modeling adaptations for filamentary and interface switching RRAM mechanisms.

Main Results:

• Comprehensive overview of existing RRAM modeling techniques.
• Detailed discussion on the applicability of various models to different RRAM switching types.
• Illustrative examples of compact modeling for simulating RRAM-based computing circuits.

Conclusions:

• Modeling is essential for advancing RRAM technology towards industrial adoption.
• The reviewed modeling approaches provide a foundation for device optimization and circuit design.
• Compact modeling is a viable tool for simulating RRAM circuits for next-generation computing applications.