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Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing.

Seung Ju Kim^1,2, In Hyuk Im¹, Ji Hyun Baek¹

¹Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, Republic of Korea.

Nature Nanotechnology

|October 18, 2024

Summary

Two-dimensional (2D) perovskite artificial synapses demonstrate highly linear and symmetrical conductance changes, overcoming limitations of previous materials for neuromorphic computing. This breakthrough enables stable, high-accuracy artificial synaptic devices for advanced computing applications.

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Area of Science:

Materials Science
Neuroscience
Electronics

Background:

Three-dimensional (3D) halide perovskites show promise for memristive applications but suffer from poor moisture stability and difficult ion transport control.
Two-dimensional (2D) halide perovskites offer improved moisture resistance and anisotropic properties, yet exhibit asymmetrical and nonlinear conductance changes, limiting neuromorphic hardware efficiency.

Purpose of the Study:

To achieve highly linear and symmetrical conductance changes in Dion-Jacobson 2D perovskites for artificial synapses.
To develop and evaluate a 7x7 crossbar array of perovskite-based artificial synapses for neuromorphic hardware applications.

Main Methods:

Fabrication of Dion-Jacobson 2D perovskite artificial synapses.
Construction of a 7x7 crossbar array utilizing these perovskite synapses.
Experimental analysis and first-principles calculations to understand device performance mechanisms.

Main Results:

Achieved highly linear and symmetrical conductance changes in 2D perovskite synapses.
Demonstrated a 7x7 crossbar array with high device yield, low variation, long retention, and over 7 months of moisture stability.
Simulations showed image inference accuracy within 0.08% of the theoretical limit.

Conclusions:

Dion-Jacobson 2D perovskites enable efficient and stable artificial synapses for neuromorphic computing.
Elimination of gaps between inorganic layers facilitates homogeneous halide vacancy migration, crucial for device performance.
These perovskite synapses offer a viable path towards scalable, high-performance artificial intelligence hardware.