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Self-Rectifying Memristors Based on Dimensionally Graded Halide Perovskites.

Divyam Sharma1, Subham Paramanik1, Dong Shuai2

  • 1School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue, Singapore, Singapore.

Advanced Materials (Deerfield Beach, Fla.)
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Summary

Researchers developed self-rectifying halide memristors using dimensionally graded perovskites to solve sneak-path issues in neuromorphic computing. This breakthrough enables more efficient and accurate in-memory computing for AI applications.

Keywords:
2D/3D perovskiteartificial synapsecrossbar arraysself‐rectifying memristorsneak path

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Area of Science:

  • Materials Science
  • Electronics Engineering
  • Computer Science

Background:

  • Neuromorphic in-memory computing addresses the data deluge in AI and smart electronics.
  • Memristor crossbar arrays are key hardware for in-memory computing.
  • Halide perovskites offer potential for memristors due to their properties, but sneak-path issues hinder performance.

Purpose of the Study:

  • To develop self-rectifying halide memristors to overcome sneak-path challenges in memristor crossbar arrays.
  • To create a dimensionally graded perovskite structure for improved memristor functionality.
  • To enhance the efficiency and accuracy of neuromorphic in-memory computing.

Main Methods:

  • Fabrication of a 2D to 3D dimensionally graded halide perovskite memristor.
  • Engineering a heterojunction using specific 2D spacer cations and methylammonium lead iodide.
  • Characterization of device rectification, endurance, linearity, and performance in a large crossbar array.

Main Results:

  • Achieved a high rectification ratio greater than 10^3.
  • Demonstrated robust synaptic characteristics with endurance exceeding 4 × 10^4 pulses and high linearity in weight updates.
  • Successfully suppressed sneak currents, enabling a 140 × 140 crossbar array with 93% accuracy in image classification despite write noise.

Conclusions:

  • The novel dimensionally graded perovskite memristor effectively solves the sneak-path problem.
  • This self-rectifying memristor technology is suitable for large-scale neuromorphic computing arrays.
  • The approach significantly advances the potential of halide perovskites in AI hardware.