Thermal Annealing-Driven Modulation of Charge Trapping and Synaptic Plasticity in a Sol-Gel AlO-Based Floating Gate...

Area of Science:

• Materials Science
• Electronics Engineering
• Nanotechnology

Background:

• High-performance synaptic devices and organic floating gate memory are crucial for next-generation electronics, enabling low power consumption and high-density data storage.
• Effective charge trapping layers are essential for these devices, but suitable materials remain a challenge.
• Solution-processed aluminum oxide (AlO) offers a cost-effective and flexible alternative for charge trapping applications.

Purpose of the Study:

• To investigate the properties of sol-gel aluminum oxide (AlO) thin films annealed at various temperatures for use as charge trapping layers.
• To evaluate the performance of sol-gel AlO-based floating gate transistors in simulating synaptic behavior.
• To optimize annealing temperature for enhanced synaptic characteristics like nonlinearity (NL) and dynamic range (DR).

Main Methods:

• Sol-gel processing of AlO thin films.
• Annealing of films at temperatures ranging from pristine to 500 °C.
• Characterization using X-ray diffraction, atomic force microscopy, and X-ray photoelectron spectroscopy.
• Fabrication and testing of floating gate transistors for synaptic behavior simulation, including analysis of NL and DR.
• Deep neural network simulation using the MNIST dataset to assess device accuracy.

Main Results:

• Annealing AlO films led to the decomposition of organic residues and transformation into aluminum oxide, with hydroxyl groups influencing hysteresis.
• Devices annealed above 200 °C exhibited improved NL and DR characteristics.
• The AlO-based device annealed at 200 °C achieved a high accuracy of ~93.60% in MNIST simulations at a pulse width of 200 ms.

Conclusions:

• Annealing temperature is a critical parameter for optimizing the performance of sol-gel AlO in synaptic devices.
• The 200 °C annealed AlO thin films demonstrate significant potential for advanced electronic applications requiring efficient synaptic simulation.
• This work contributes to the development of more efficient, flexible, and dense electronic devices for memory and neuromorphic computing.