Photoassisted Electroforming Method for Reliable Low‐Power Organic–Inorganic Perovskite Memristors

Abstract

Organic-inorganic hybrid perovskite memristors with high resistiveswitching (RS) reliability and low power consumption are crucial for high-density storage and high-efficiency neuromorphic computing. However, the current overshoot in the electroforming process generally induces overgrowth of conductive filaments (CFs) and degrades the RS performance. Here, a simple photo-assisted electroforming (PAE) method to suppress the current overshoot, in which the visible light irradiation is introduced into the initial electroforming process, is proposed for the first time. As a result, a reliable memristor with reduced RS fluctuation and enhanced cycling endurance is obtained, and also, the low operating current of 0.06 mA and low powerconsumption of 0.12 mW are achieved, which are about one order of magnitude lower than those of most reported hybrid perovskite-based memristors. Further experimental evidence indicates that light irradiation plays dual roles: 1) the lightinduced lowering of iodide migration barrier leads to a significant reduction of overshoot current and forming voltage; 2) the enhanced local photoconductivity of the perovskite film shares the overshoot current through the CFs. Both factors limit the total quantity of vacancy defects generated in the electroforming process, thus preventing undesirable overgrowth of the CFs. The present PAE strategy has promise for developing high-performance memristors.

Mixed Conductivity of Hybrid Halide Perovskites: Emerging Opportunities and Challenges

“…Another possible reason that can lead to 'memristive' behavior, which has to the best of our knowledge not yet been discussed in hybrid perovskites, is the formation of ferroelectric tunneling barriers (Chanthbouala et al, 2012) that remain to be explored. Moreover, light illumination can further suppress and even reverse the resistive switching behavior due to a reduced halide migration barrier ( Figure 2D) (Zhu et al, 2017;Zhao et al, 2020). This opens the possibility of devices that can be set, reset, modified, and read out using a combination of electrical and optical stimuli.…”

Zeolite‐Based Memristive Synapse with Ultralow Sub‐10‐fJ Energy Consumption for Neuromorphic Computation

“…Synaptic emulation with ultralow power consumption can be achieved using a low operating current and fast switching. [22][23][24][28][29][30] It is confirmed that I cc reduces to 0.1 µA during the activation process thanks to the ultrathin confined Ag channel, as shown in Figure 2a-(ii), resulting in a low operating current (<1 µA). Furthermore, owing to the low activation barrier of Ag ion migration (<0.2 eV) in Ag-LTA zeolite, [49,50] the pulse width of the synaptic spike reduces from 10 ms to a fast operating speed of 10 ns (see Figure S7 in the Supporting Information).…”

“…[7][8][9][10] Ion drift memristors, which rely on electrochemical metallization (ECM) of the active electrode (e.g., Ag or Cu), have a fast switching speed of tens of nanoseconds. [22][23][24] For a memristive switching mechanism, the morphology modification of conductive filaments (CFs) can generally account for the change in device conductance. [25][26][27] The operating current is mainly determined by the CF dimension, and thin CFs are thus required for ultralow switching power.…”

Memristor-based Chaotic System with Upper-Lower Chaotic Attractors and Abundant Dynamical Behaviors

“…(30). Here, we keep the system(1) parameters a = 2, b = 8.2, c=5, d = 3, g = 5, r=10000 and set the initial condition (8,3,6,2).Thus, the Lyapunov exponents of system (1) are L1=0.5694, L2=0.0020≈0, L3=-4.9924 and L4=-13.3474, which ensures chaotic properties of the system. Taking smooth function f (t) =sin (2t) as example, the time-domain results of u1, u2, u3, u4 are shown in Fig.…”

“…After that, memristor has attracted researchers more attention all over the world. The memristor is expected to have a wide range of applications thanks to its memory properties, such as information storage [3], neural networks [4], and brain-like computers [5].…”