Issue 8, 2025

Integrated artificial neurons from metal halide perovskites

Abstract

Hardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. Artificial neurons are a key component of these networks and are currently implemented with electronic circuits based on capacitors and transistors. However, artificial neurons based on memristive devices are a promising alternative, owing to their potentially smaller size and inherent stochasticity. But despite their promise, demonstrations of memristive artificial neurons have so far been limited. Here we demonstrate a fully on-chip artificial neuron based on microscale electrodes and halide perovskite semiconductors as the active layer. By connecting a halide perovskite memristive device in series with a capacitor, the device demonstrates stochastic leaky integrate-and-fire behavior, with an energy consumption of 20 to 60 pJ per spike, lower than that of a biological neuron. We simulate populations of our neuron and show that the stochastic firing allows the detection of sub-threshold inputs. The neuron can easily be integrated with previously-demonstrated halide perovskite artificial synapses in energy-efficient neural networks.

Graphical abstract: Integrated artificial neurons from metal halide perovskites

Supplementary files

Article information

Article type
Communication
Submitted
29 নবে 2024
Accepted
16 ডিচে 2024
First published
20 জানু 2025
This article is Open Access
Creative Commons BY license

Mater. Horiz., 2025,12, 2701-2708

Integrated artificial neurons from metal halide perovskites

J. J. de Boer and B. Ehrler, Mater. Horiz., 2025, 12, 2701 DOI: 10.1039/D4MH01729C

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