On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits.

Area of Science:

• Materials Science and Engineering
• Neuroscience
• Computer Engineering

Background:

• Memristive devices mimic neuronal synapses, making them suitable for neuromorphic circuits and memory applications.
• Challenges exist in obtaining and precisely controlling memristor samples due to fabrication difficulties and variability.
• Simulation and emulation are common alternatives, both requiring accurate memristive behavior models.

Purpose of the Study:

• To present an experimentally validated, diffusive compact model for memristive behavior.
• To implement an emulation architecture for exploring memristor synapse-like characteristics.
• To provide insights into desirable memristor properties for neuromorphic applications.

Main Methods:

• Development and experimental validation of a diffusive compact model for memristors.
• Implementation of an emulation architecture for memristive systems.
• Comparison of emulation advantages over traditional simulation, highlighting real-world circuit interaction.

Main Results:

• A validated diffusive compact model accurately represents memristive behavior.
• The emulation architecture allows flexible investigation of synapse-like properties.
• Emulation offers benefits over simulation by integrating with physical circuits.

Conclusions:

• The presented model and emulation architecture facilitate the study of memristors for neuromorphic applications.
• This approach mitigates issues related to memristor sample availability and variability.
• Findings offer guidance for designing future memristors tailored for brain-inspired computing.