Enhanced 2D MoTe₂ Analogue Switching through Laser Processing and ALD-Passivation for Dual-Function Neuromorphic...

Area of Science:

• Materials Science
• Nanotechnology
• Neuro-engineering

Background:

• Memristors and memtransistors are key for neuro-based computing.
• Two-dimensional molybdenum ditelluride (MoTe2) shows promise due to its low phase-change energy and tunable electrical properties.
• Pristine MoTe2 devices have limited memory windows, hindering neuromorphic applications.

Purpose of the Study:

• To enhance the analog switching characteristics of lateral MoTe2 devices.
• To improve the performance of MoTe2-based memristors and memtransistors for neuromorphic computing.
• To investigate the synergistic effects of sequential laser treatment and Al2O3 atomic layer deposition.

Main Methods:

• Sequential application of laser treatment and atomic layer deposition (ALD) of Al2O3 on lateral MoTe2 devices.
• Characterization of device performance as both memristors and memtransistors.
• Simulation of artificial neural networks to evaluate pattern recognition accuracy.

Main Results:

• A 70-fold improvement in dynamic range for memristor operation.
• A 20-fold enhancement in dynamic range for memtransistor operation.
• A 10-fold increase in pattern recognition accuracy in artificial neural network simulations.

Conclusions:

• The combined laser treatment and Al2O3 ALD effectively enhances MoTe2 device performance for neuromorphic applications.
• The dual memristor/memtransistor functionality enables emulation of synaptic plasticity (homosynaptic and heterosynaptic).
• This approach significantly advances the potential of 2D materials in next-generation computing architectures.