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Modulating Trapping in Low-Dimensional Lead-Tin Halides for Energy-Efficient Neuromorphic Electronics.

Lijun Chen1,2, Saad Saleh3,2, Filippo Tavormina1,4

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Summary
This summary is machine-generated.

This study introduces a lead-free perovskite memristor for neuromorphic computing, demonstrating high endurance and accuracy on the MNIST dataset. A novel, energy-efficient content addressable memory architecture is also proposed, significantly reducing power consumption.

Keywords:
2D layered Pb─Sn perovskiteanalog CAMneuromorphic computingtrap states

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Neuroscience

Background:

  • Metal halide perovskites show promise for neuromorphic electronics.
  • Challenges include lead toxicity, device variability, and high energy consumption.

Purpose of the Study:

  • To develop a lead-free perovskite memristive device for neuromorphic applications.
  • To improve device performance and energy efficiency.
  • To propose a novel content addressable memory (CAM) architecture.

Main Methods:

  • Fabrication of a 2D Ruddlesden-Popper (RP) perovskite system: BA2Pb0.5Sn0.5I4.
  • Cesium carbonate (Cs2CO3) deposition to enhance resistive switching.
  • Implementation and testing in a content addressable memory (CAM) architecture (nCAM).

Main Results:

  • The device exhibits excellent switching characteristics: endurance of 5 × 10^5 cycles and an ON/OFF ratio of approximately 10^5.
  • Achieved 90.1% accuracy on the MNIST dataset.
  • Proposed nCAM architecture demonstrates a minimum energy consumption of ≈0.025 fJ/bit/cell.

Conclusions:

  • The developed perovskite memristor offers a viable, lead-free alternative for neuromorphic computing.
  • Cesium carbonate deposition effectively improves resistive switching by manipulating trapping states.
  • The novel nCAM architecture significantly enhances energy efficiency in neuromorphic applications.