Highly Reliable and Uniform Synaptic Transistors Enabled by a Polymer-Engineered Semiconducting Nanotube Network

Area of Science:

• Materials Science
• Nanotechnology
• Neuroscience

Background:

• Developing advanced materials for neuromorphic computing is crucial for next-generation AI.
• Single-walled carbon nanotubes (SWCNTs) offer promising electronic properties but require precise functionalization for device applications.
• Synaptic transistors mimic biological synapses, enabling efficient information processing.

Purpose of the Study:

• To demonstrate a high-performance synaptic transistor array using a novel polymer-nanotube hybrid structure.
• To investigate the synaptic plasticity and neuromorphic functionalities of the developed device.
• To evaluate the device's potential for artificial neural network applications.

Main Methods:

• Fabrication of an 8x8 synaptic transistor array using poly(9,9-di-n-dodecylfluorenyl-2,7-diyl) (PFDD)-wrapped semiconducting single-walled carbon nanotubes (s-SWCNTs).
• Characterization of the PFDD-SWCNT hybrid structure for controlled conductance modulation and uniform electrical properties.
• Emulation of diverse synaptic functions including excitatory post-synaptic currents, short- and long-term memory, LTP/LTD, and paired-pulse facilitation.
• Testing device stability over 10,000 pulse endurance cycles and evaluating performance in handwritten image classification simulations.

Main Results:

• The PFDD-SWCNT hybrid structure provides selective sorting of s-SWCNTs and stable charge-trapping sites, enabling reproducible conductance tuning.
• The synaptic transistors exhibit robust synaptic plasticity, successfully emulating various synaptic functions.
• The device demonstrated stable modulation of long-term potentiation/depression over 10,000 cycles.
• Low nonlinearity and high asymmetry ratio resulted in a 90.26% recognition accuracy in handwritten image classification simulations.

Conclusions:

• The PFDD-SWCNT hybrid structure offers a robust platform for high-precision and reliable neuromorphic circuitry.
• This material system enables efficient emulation of synaptic functions with excellent stability and performance.
• The developed synaptic transistors show significant potential for advancing artificial intelligence hardware.