Tightly coupled charge transport properties of halide perovskites and their applications in synaptic memristors
Shu Zhou a
a School of Materials, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
Proceedings of Device Physics Characterization and Interpretation in Perovskite and Organic Materials (DEPERO)
València, Spain, 2023 October 3rd - 5th
Organizers: Sandheep Ravishankar, Juan Bisquert and Evelyne Knapp
Invited Speaker, Shu Zhou, presentation 016
Publication date: 14th September 2023

Halide perovskite (ABX3) has recently achieved great success in the field of electronic devices beyond photovoltaics. Compared to conventional memristive metal oxides, halide perovskite possesses multiple chemical elements, abundant defect types, and complete defect chemistry theory that are desired for high-performance memristors. The metal-halide electrochemical reaction mediated by migration of ion defects, the formation of lead clusters, together with the valence-change mechanism driven by halide vacancy can not only meet the demands of two-state, multi-state or even continuous and reversible switching of resistance, but also provide an ideal material model for study of the physical mechanism of memristors.

Here, the unique ionic-electronic coupled transport properties of nanosized perovskite films will be discussed first. The relation between the microscopic transport mechanism and the macroscopic resistance switching characteristic will be unraveled. These perovskite films are then employed to construct memristors from the bottom up, demonstrating low energy consumption and many important synaptic features. A coupled capacitive and inductive phenomenon originating from charge trapping and ion migration, controlled by amplitude and timing of the programming pulses, is observed for defining the degree of synaptic plasticity. To further improve the performance, a two-terminal synaptic memristor based on phase-pure and well-structured single crystals of perovskites is fabricated. The device exhibits extremely low power consumption (~26 fJ) on par with that of a bio-synapse. In particular, the synapse-like potentiation and depression under continuous stimulations can be reduplicated for more than 20000 cycles with excellent reproducibility, which makes the device one of the most stable and energy efficient artificial synapses reported to date. Finally, inspired by the device behaviors a simple proof-of-concept demonstration of the potential for neuromorphic computing is performed, i.e., classification application using the devices comprising neuron network.

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