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Bio-inspired artificial synapse for neuromorphic computing based on NiO nanoparticle thin film.
Keval Hadiyal1,2, Ramakrishnan Ganesan3, A Rastogi1
1Centre for Functional Materials, Vellore Institute of Technology, Vellore, TN, 632014, India.
Scientific Reports
|May 10, 2023
Summary
Researchers developed a novel analog resistive switching device using nickel oxide (NiO) nanoparticles for efficient neuromorphic computation. This device mimics biological synapses, showing reliable potentiation and depression for learning and memory applications.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- The increasing demand for data processing necessitates advanced computing paradigms like neuromorphic devices.
- Neuromorphic computation leverages the brain's parallel processing for efficient data handling and decision-making.
- Reliable, room-temperature resistive switching devices are crucial for fabricating practical neuromorphic systems.
Purpose of the Study:
- To investigate a novel analog resistive switching device based on gold/nickel oxide nanoparticles/gold (Au/NiO nanoparticles/Au).
- To demonstrate the device's capability to mimic synaptic plasticity, including long-term potentiation and depression.
- To assess the potential of NiO nanoparticle-based devices for learning-forgetting-relearning characteristics in neuromorphic applications.
Main Methods:
- Fabrication of an analog resistive switching device using Au/NiO nanoparticles/Au structure.
- Application of positive and negative voltage pulses to induce changes in synaptic current.
- Analysis of conductance changes using double exponential growth and decay models.
- Evaluation of long-term potentiation (LTP) and long-term depression (LTD) characteristics.
Main Results:
- The Au/NiO nanoparticles/Au device exhibited reliable analog resistive switching behavior at room temperature.
- Synaptic current enhancement (potentiation) and reduction (depression) were observed upon application of voltage pulses.
- Conductance changes were accurately modeled by double exponential functions, indicating synaptic plasticity.
- Consistent LTP and LTD characteristics were established, essential for biological synaptic mimicry.
- The device demonstrated controlled synaptic enhancement and learning-forgetting-relearning behavior.
Conclusions:
- The developed NiO nanoparticle-based resistive switching device offers a promising platform for neuromorphic computation.
- The device effectively mimics biological synaptic functions, enabling applications in artificial learning and memory.
- Optimization of electric pulses allows for controlled synaptic enhancement, paving the way for advanced neuromorphic systems.
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