Investigations on Resistive Switching Mechanisms of Fluorinated Ruddlesden–Popper Perovskite-Based Memristors for Nociceptor Application

Ruddlesden–Popper (RP) phase hybrid organic–inorganic perovskites have gained significant attention for various optoelectronic applications due to their exceptional properties and potential for high performance. In this study, we present a diffusive memristor using a (4F-PEAI)2(MA)n–1PbnI3n+1based RP perovskite thin film as the resistive switching medium and explore its optical, structural, and electrical characteristics. The current–voltage (I–V) behavior of the device (PET/ITO/PEDOT:PSS/RP/PMMA) shows a strong dependence on the stoichiometry, particularly the number of PbI6 octahedral layers (n) present between consecutive 4F-PEA layers. By varying the molar concentration of precursor solutions, we demonstrate that higher values of MA:Pb ratio in the precursor solutions lead to a mixed-phase material with the n = 1 phase dominating. The phase distribution within the RP film influences both the SET/RESET voltages and the ON/OFF current ratio in the I–V characteristics. An optimal combination of phases with both lower and higher values of n is identified, yielding a memristor with superior switching performance, achieving an current ON/OFF ratio of approximately 104 and a SET voltage of around 1.3 V. The integration of interface layers results in current rectification of approximately 103, which is crucial for crossbar systems where low sneak currents are essential. The I–V characteristics exhibit distinct conduction mechanisms in the various regimes. Additionally, the unipolar I–V behavior with a high ON/OFF ratio makes these diffusive memristors suitable for bioinspired applications. We demonstrate a bioinspired injury-response system combining a piezoelectric pressure sensor, a memristor-based nociceptor, and optical indicators that visually represent pain intensity, enabling damage detection, real-time communication, and responsive actions. This system holds potential for advancing human–robot interaction technologies.