Enhancing Long-Term Memory in Carbon-Nanotube-Based Optoelectronic Synaptic Devices for Neuromorphic Computing

Area of Science:

• Materials Science
• Nanotechnology
• Device Physics

Background:

• Optoelectronic synaptic devices mimic biological synapses for artificial intelligence.
• Carbon nanotubes (CNTs) offer promising properties for synaptic device applications.
• Controlling film morphology is crucial for device performance.

Purpose of the Study:

• To investigate the effect of spin-coating speed on CNT film properties.
• To evaluate the impact of these properties on optoelectronic synaptic device performance, particularly long-term memory.
• To determine optimal fabrication parameters for enhanced device functionality.

Main Methods:

• Fabrication of CNT films using varying spin-coating speeds.
• Characterization of CNT film thickness, density, and persistent photoconductivity.
• Measurement of excitatory postsynaptic currents (EPSCs) and memory retention in synaptic devices.
• Assessment of device learning efficiency under UV stimulation.

Main Results:

• Lower spin-coating speeds (e.g., 2000 RPM) produced thicker, denser CNT films.
• Denser films exhibited enhanced persistent photoconductivity and higher EPSCs.
• Devices fabricated with lower spin speeds demonstrated superior long-term memory retention (EPSCs > 70% for 3600 s).
• Learning efficiency improved with repeated UV stimulation cycles.

Conclusions:

• Spin-coating speed is a critical parameter for optimizing CNT-based synaptic devices.
• Optimized fabrication leads to enhanced memory retention and learning capabilities.
• These findings support the development of advanced CNT synaptic devices for neuromorphic computing and artificial neural networks.