Spectral Evolution and Physical Model of Perovskite-based Memristors
Cedric Gonzales a, Antonio Guerrero a, Juan Bisquert a
a Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain
Proceedings of Materials, devices and systems for neuromorphic computing 2022 (MatNeC22)
Groningen, Netherlands, 2022 March 28th - 29th
Organizers: Jasper van der Velde, Elisabetta Chicca, Yoeri van de Burgt and Beatriz Noheda
Poster, Cedric Gonzales, 029
Publication date: 23rd February 2022

Metal halide perovskite semiconductors are known to exhibit mixed ionic-electronic conduction causing intrinsic memory effects (hysteresis) in the current-voltage (I-V ) making them highly significant in memory applications as artificial synapses in neuromorphic computation. [1-3] The evolution of device properties in memristor switching between high and low resistance states is critical for applications and is still highly subjected to significant ambiguity. Here, we present the dynamic state transition in a 2D Ruddlesden-Popper perovskite-based memristor device, measured via impedance spectroscopy (IS). [4] The spectral evolution of the transition exhibits a significant transformation of the low frequency arc to a negative capacitance (inductive) arc, further decreasing the device resistance. The interfacial reactivity between the perovskite and Ag metal contact resulted to a gradual state transition, indicative of a non-filamentary switching mechanism. In contrast, a thin, undoped Spiro-OMeTAD interfacial layer impeded the reactivity between the migrating ions and the metal contact exhibiting an abrupt state transition suggesting the filament formation within the Spiro-OMeTAD layer. A physical model of the kinetic behavior of a more established MAPbI3-based memristor describes both the I-V  and IS response providing a physical insight into the coupling of ionic and electronic properties that produce the resistive switching behavior. [1] This insight in the dynamic state transition investigated by IS, in conjunction with the physical dynamic model, would allow for artificial synapse designs tailored for a wide range of neuromorphic applications.

We thank the financial support from Generalitat Valenciana for a Prometeo (No. PROMETEU/2020/028), Grisolia Grant (No. GRISOLIAP/2019/048), and Ministerio de Ciencia y Innovaci?on (No. PID2019-107348GB-100).

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