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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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TCAD Simulation of Resistive Switching Devices: Impact of ReRAM Configuration on Neuromorphic Computing.

Seonggyeom Kim¹, Jonghwan Lee¹

¹Department of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Republic of Korea.

Nanomaterials (Basel, Switzerland)

|December 17, 2024

Summary

This study models resistive switching memory (ReRAM) using TCAD and kinetic Monte Carlo simulations. The validated ReRAM model accurately predicts device behavior for neuromorphic computing applications.

Keywords:

KMC (kinetic Monte Carlo)ReRAM (resistive random-access memory)TCAD (technology computer-aided design)memristor neuromorphic computing

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Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Accurate modeling of Resistive Random-Access Memory (ReRAM) is crucial for developing reliable neuromorphic systems.
Existing models may not fully capture the complex switching dynamics and device variations inherent in ReRAM.

Purpose of the Study:

To develop and validate a TCAD-based model for ReRAM simulation.
To assess the accuracy of the ReRAM model in the context of neuromorphic systems.
To investigate the impact of material composition and structure on ReRAM switching behavior.

Main Methods:

Utilized Technology Computer-Aided Design (TCAD) for device simulation.
Implemented ReRAM switching behaviors using the kinetic Monte Carlo (KMC) approach.
Incorporated trap-assisted tunneling (TAT) model and thermal equations for realistic device characteristics.

Main Results:

HfO2-Al2O3-based ReRAM demonstrated improved switching characteristics compared to HfO2-based ReRAM.
ReRAM conductance variation was found to be dependent on device structure.
TCAD-derived conductance values were successfully validated within a neuromorphic system using the MNIST dataset.

Conclusions:

The developed TCAD model accurately represents ReRAM switching behavior.
The validated model is suitable for analyzing neuromorphic system performance.
Material engineering and structural design significantly influence ReRAM performance for neuromorphic applications.