Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic...

Area of Science:

• Materials Science
• Nanotechnology
• Neuroscience

Background:

• Memristor devices are crucial for neuromorphic computing.
• Developing devices with tunable synaptic behavior is essential for advanced AI hardware.

Purpose of the Study:

• To report on hybrid memristor devices incorporating germanium dioxide nanoparticles (GeO2 NP) in a poly(methyl methacrylate) (PMMA) matrix.
• To investigate the unique switching characteristics and bifunctional synaptic behavior of these nanocomposite devices.

Main Methods:

• Fabrication of hybrid memristor devices with embedded GeO2 NPs in a PMMA thin film.
• Characterization of resistive switching behavior, focusing on stopping voltage-dependent switching.
• Demonstration of emulation of various synaptic functions in both short-term and long-term memory modes.

Main Results:

• The hybrid devices exhibit forming-free resistive switching and an unusual ON state in pristine conditions.
• A novel mixed-mode switching behavior was observed, dependent on the stopping voltage.
• The devices successfully emulate diverse synaptic functions, including learning-forgetting-rehearsal and plasticity, in switchable short-term and long-term memory modes.

Conclusions:

• The co-existence of short-term and long-term resistive switching modes in a single device offers unprecedented flexibility.
• These findings pave the way for designing adaptive and reconfigurable neuromorphic computing systems at the hardware level.
• The developed hybrid memristors represent a significant advancement in materials for next-generation AI hardware.