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Synaptic κ-Ga2O3 Photodetectors for Privacy-Enhancing Neuromorphic Computing.

Yanqing Jia1, Heming Lin1, Hongliang Chang1

  • 1Photonics Laboratory, Electrical and Computer Engineering, Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

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Summary
This summary is machine-generated.

Researchers developed a novel neuromorphic photodetector using gallium oxide. This device integrates sensing, memory, and computation for intelligent edge systems, enabling secure authentication and efficient AI inference directly within the hardware.

Keywords:
hardware‐level authenticationneuromorphic inference capabilityneuromorphic photodetectorκ‐Ga2O3

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Area of Science:

  • Optoelectronics
  • Materials Science
  • Artificial Intelligence

Background:

  • Intelligent edge systems require integrated sensing, memory, and computation.
  • Persistent photoconductivity (PPC) in materials offers potential for neuromorphic applications.
  • Gallium oxide (Ga2O3) is a promising semiconductor for advanced electronic devices.

Purpose of the Study:

  • To develop a multifunctional neuromorphic photodetector using κ-phase gallium oxide (κ-Ga2O3).
  • To demonstrate in-sensor information processing, including authentication and artificial neural network (ANN) inference.
  • To explore the potential of κ-Ga2O3 for compact, intelligent, and privacy-enhancing edge systems.

Main Methods:

  • Fabrication of metal-semiconductor-metal photodetectors based on κ-Ga2O3 exhibiting persistent photoconductivity (PPC).
  • Exploitation of device-distinguishable photocurrent responses for hardware-level authentication using a 1D deep embedding network.
  • Evaluation of neuromorphic inference using a hardware-aware simulation framework, mapping experimental conductance states to a quantization-aware trained ANN.
  • Conversion of the ANN to a leaky integrate-and-fire (LIF) spiking neural network (SNN) for device-constrained operation.

Main Results:

  • The κ-Ga2O3 photodetectors demonstrated reproducible yet device-distinguishable temporal photocurrent responses due to the pronounced PPC effect.
  • Hardware-level authentication achieved an Area Under the Curve (AUC) of approximately 0.97 and an Equal Error Rate (EER) of about 9%.
  • The quantized ANN achieved 98.17% accuracy, and the converted LIF SNN retained 96.80% accuracy.

Conclusions:

  • The developed κ-Ga2O3 synaptic photodetectors enable integrated sensing, authentication, and inference at the device level.
  • This materials-enabled approach offers a pathway toward compact, intelligent, and privacy-enhancing optoelectronic hardware for edge systems.
  • The study highlights the potential of persistent photoconductivity in κ-Ga2O3 for advanced neuromorphic computing applications.