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All-Inorganic CsPbBr Perovskite Planar-Type Memristors as Optoelectronic Synapses. | LitMetric

All-Inorganic CsPbBr Perovskite Planar-Type Memristors as Optoelectronic Synapses.

ACS Appl Mater Interfaces

Center for Optics Research and Engineering, Shandong University, Qingdao 266237, P. R. China.

Published: September 2024

AI Article Synopsis

  • * A new type of memristor using CsPbBr single crystal is introduced, showcasing remarkable qualities like stable performance, low power use, and fast switching capabilities, along with adjustable performance based on light conditions.
  • * This memristor effectively simulates complex brain functions, such as learning and memory processes, and achieves high accuracy in pattern recognition tasks with artificial neural networks, suggesting a promising future for energy-efficient neuromorphic electronics.

Article Abstract

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning-forgetting-relearning process with ultralow per synapse event energy consumption. A classical Pavlov's dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.

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Source
http://dx.doi.org/10.1021/acsami.4c09673DOI Listing

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