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Ultralow Energy Optoelectronic Synapse Using Halide Perovskite/Organic Semiconductor Heterostructure for Neuromorphic

Debabrata Sahu1, Sanju Nandi1, Garima Choudhary1

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ACS Applied Materials & Interfaces
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Summary
This summary is machine-generated.

This study presents an energy-efficient optoelectronic synapse for neuromorphic computing, demonstrating learning, memory, and pattern recognition with low power consumption. The device shows promise for advanced artificial intelligence systems and optical communication.

Keywords:
halide perovskiteneuromorphic computingoptical wireless communicationoptoelectronic synapseorganic semiconductor

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Neuromorphic computing aims to emulate biological neural processes for advanced AI.
  • Artificial optoelectronic synapses offer a solution to overcome the von Neumann bottleneck with low energy consumption.
  • Perovskite/polymer heterojunctions are promising for developing efficient synaptic devices.

Purpose of the Study:

  • To design and demonstrate a multifunctional, energy-efficient optoelectronic synapse.
  • To investigate the synaptic characteristics and learning behaviors of the device.
  • To explore applications in visual object recognition, light logic, optical communication, and semantic segmentation.

Main Methods:

  • Fabrication of a formamidinium cesium lead iodide (FAxCs1-xPbI3)/Poly(3-hexylthiophene) (P3HT) heterojunction optoelectronic synapse.
  • Characterization of synaptic behaviors including excitatory postsynaptic current (EPSC) and paired-pulse facilitation (PPF).
  • Implementation in artificial neural networks (ANN) for MNIST dataset recognition and U-Net models for semantic segmentation, alongside optical communication experiments.

Main Results:

  • The device exhibited key synaptic characteristics and a transition from short-term to long-term memory with 0.59 fJ energy consumption per event.
  • Demonstrated biological learning behaviors (learning-forgetting-relearning), efficient visual object recognition (90.31% accuracy on MNIST), and light logic functions.
  • Achieved 86.76% pixel-wise accuracy in urban street scene semantic segmentation and successful optical wireless communication using Morse code.

Conclusions:

  • The developed optoelectronic synapse is highly energy-efficient and multifunctional, suitable for advanced neuromorphic computing.
  • The device emulates biological learning and shows potential for diverse applications including AI, optical communication, and intelligent systems.
  • Understanding the working mechanism provides insights into optoelectronic plasticity, paving the way for integrated photonic neuromorphic devices.