Selective UV Sensing for Energy-Efficient UV-A Artificial Synapses Using a ZnO/ZnGa₂O₄ Heterojunction Diode

Area of Science:

• Materials Science
• Optoelectronics
• Neuromorphic Computing

Background:

• Neuromorphic computing systems offer efficient parallel data storage and processing.
• Artificial optical synapses are key for advanced in-memory computing technologies.
• Developing selective UV detectors is crucial for optical synapse applications.

Purpose of the Study:

• To demonstrate a high-performance UV detector for artificial optical synapse applications.
• To investigate the pyrophototronic effect in ZnO/ZnGa2O4 heterostructures for synaptic functions.
• To achieve selective UV-A and UV-C detection.

Main Methods:

• Fabrication of a ZnO/ZnGa2O4 heterostructure.
• Characterization of UV detection capabilities (UV-A and UV-C).
• Analysis of the pyrophototronic and photogating effects for synaptic activity simulation.

Main Results:

• Achieved a responsivity of 407 A/W for the UV detector.
• Demonstrated selective UV-A detection over UV-C via the pyrophototronic effect.
• Showcased light-tunable synaptic plasticity and persistent photoconductivity.

Conclusions:

• ZnO/ZnGa2O4 heterostructures can function as electro-photoactive synapses.
• The pyrophototronic effect enables UV-A selective detection and synaptic learning/forgetting.
• These findings pave the way for thermal dynamics-controlled artificial optoelectronic synapse systems.