Recently, organometallic and all-inorganic halide perovskites (HPs) have become promising materials for resistive switching (RS) nonvolatile memory devices with low power consumption because they show current–voltage hysteresis caused by fast ion migration. However, the toxicity and environmental pollution potential of lead, a common constituent of HPs, has limited the commercial applications of HP-based devices. Here, RS memory devices based on lead-free all-inorganic cesium tin iodide (CsSnI3) perovskites with temperature tolerance are successfully fabricated. The devices exhibit reproducible and reliable bipolar RS characteristics in both Ag and Au top electrodes (TEs) with different switching mechanisms. The Ag TE devices show filamentary RS behavior with ultralow operating voltages (<0.15 V). In contrast, the Au TE devices have interface-type RS behavior with gradual resistance changes. This suggests that the RS characteristics are attributed to either the formation of metal filaments or the ion migration of defects in HPs under applied electric fields. These distinct mechanisms may permit the opportunity to design devices for specific purposes. This work will pave the way for lead-free all-inorganic HP-based nonvolatile memory for commercial application in HP-based devices.

required. Several types of emerging mem ories have been researched in the past few decades such as magnetic memory, phase change memory, ferroelectric tunnel junc tions, and resistive switching memory. Among these emerging devices, resistive switching memory called memristors, introduced by Chua in 1971, [1] have strong points of small cell size, nonvolatile and random data access possibility, easy fabri cation process, and simple structure. [2,3] Because of these advantages, various mate rials are examined for achieving memris tive properties.In addition, different from the past sev eral decades, information is being made depending on experiences or repeated stimuli similar to that in the human brain. The human brain contains ≈10 11 neurons and 10 15 synapses, occupies a small space, and consumes less than 20 W, which is lower than the power required to run a household light bulb. [4][5][6] Moreover, the human brain is currently considered as the most intelligent and fastest operation system. Therefore, neuromorphic computing, which emu lates the human brain, has been regarded as a promising nextgeneration computing system. Studies on neuromorphic computing have been rapidly growing and highlighted for various applications such as artificial intelligence, sensors, robotic devices, and memory devices.Existing neural networks are implemented by the combination of machine learning as software and the von Neumann archi tecture as hardware based on the complementary metaloxide semiconductor (CMOS) technology. However, CMOSbased cir cuits require 6-12 transistors and the design is not flexible. [7] The present computing system with the von Neumann architecture is implemented by a serial operation through a central processing unit (CPU). Because of the von Neumann bottleneck, memory devices have limitations in data processing speed between memory and CPU and require high power and large space. [8][9][10] Therefore, a new neuromorphic computing system that is exe cuted by parallel operation with a high operation speed, low energy consumption, and small volume is critically required.To achieve such requirement, memristive materials have been actively examined as emulating several functions of human brain. A memristor could act as a single unit of synapse without software programming supports. Memristorbased neu romorphic architecture is implemented by parallel operation with efficient power, small volume, and high data processing Neuromorphic architectures are in the spotlight as promising candidates for substituting current computing systems owing to their high operation speed, scale-down ability, and, especially, low energy consumption. Among candidate materials, memristors have shown excellent synaptic behaviors such as spike time-dependent plasticity and spike rate-dependent plasticity by gradually changing their resistance state according to electrical input stimuli. Memristor can work as a single synapse without programming support, which remarkably satisfies the requirements of neuromorphic computing. Here, the mo...

Resistive random-access memory (ReRAM) devices based on halide perovskites have recently emerged as a new class of data storage devices, where the switching materials used in these devices have attracted extensive attention in recent years. Thus far, three-dimensional (3D) halide perovskites have been the most investigated materials for resistive switching memory devices. However, 3D-based memory devices display ON/OFF ratios comparable to those of oxide or chalcogenide ReRAM devices. In addition, perovskite materials are susceptible to exposure to air. Herein, we compare the resistive switching characteristics of ReRAM devices based on a quasi-two-dimensional (2D) halide perovskite, (PEA) 2 Cs 3 Pb 4 I 13 , to those based on 3D CsPbI 3. Astonishingly, the ON/OFF ratio of the (PEA) 2 Cs 3 Pb 4 I 13-based memory devices (10 9) is three orders of magnitude higher than that of the CsPbI 3 device, which is attributed to a decrease in the high-resistance state (HRS) current of the former. This device also retained a high ON/OFF current ratio for 2 weeks under ambient conditions, whereas the CsPbI 3 device degraded rapidly and showed unreliable memory properties after 5 days. These results strongly suggest that quasi-2D halide perovskites have potential in resistive switching memory based on their desirable ON/OFF ratio and long-term stability.

Halide-perovskites-based resistive random-access memory (ReRAM) devices are emerging as a new class of revolutionary data storage devices because the switching material—halide perovskite—has received considerable attention in recent years owing to its unique and exotic electrical, optical, and structural properties.

Neuromorphic computing, which mimics biological neural networks, can overcome the high-power and large-throughput problems of current von Neumann computing. Two-terminal memristors are regarded as promising candidates for artificial synapses, which are the fundamental functional units of neuromorphic computing systems. All-inorganic CsPbI 3 perovskite-based memristors are feasible to use in resistive switching memory and artificial synapses due to their fast ion migration. However, the ideal perovskite phase α-CsPbI 3 is structurally unstable at ambient temperature and rapidly degrades to a non-perovskite δ-CsPbI 3 phase. Here, dual-phase (Cs 3 Bi 2 I 9 ) 0.4 −(CsPbI 3 ) 0.6 is successfully fabricated to achieve improved air stability and surface morphology compared to each single phase. Notably, the Ag/polymethylmethacrylate/(Cs 3 Bi 2 I 9 ) 0.4 −(CsPbI 3 ) 0.6 /Pt device exhibits non-volatile memory functions with an endurance of ≈10 3 cycles and retention of ≈10 4 s with low operation voltages. Moreover, the device successfully emulates synaptic behavior such as long-term potentiation/depression and spike timing/widthdependent plasticity. This study will contribute to improving the structural and mechanical stability of all-inorganic halide perovskites (IHPs) via the formation of dual phase. In addition, it proves the great potential of IHPs for use in low-power non-volatile memory devices and electronic synapses.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adfm.201906686.femto-Joule scale. [1,2] Neuromorphic computing systems that emulate the human brain are considered promising candidates for overcoming the energy and throughput limitations of conventional von Neumann computing systems. Inspired by the human brain, neuromorphic computing systems are composed of electronic neurons and synapses, and achieve a high degree of parallelism with the physically united memory and information processing unit. Artificial synapses are fundamental functional units of neuromorphic architectures, where electrical stimuli from a pre-neuron are transmitted to a post-neuron, thereby generating updated synaptic weight (i.e., causing a conductance change in an electronic device). Notably, resistive switching (RS) memory has been actively investigated for synaptic devices due to its scalability, simple structure, fast operation speed, and low-energy consumption, which are the most important requirements for neuromorphic computing. [3,4] In the last few years, halide perovskites (HPs) with the chemical formula ABX 3 [where A is an organic or inorganic (Cs or Rb) cation, B is a divalent metal cation (Pb or Sn), and X is a halide anion (I, Cl, or Br)] have been widely investigated for RS memory and artificial synapses because of their tunable bandgaps and fast ion migration. [5][6][7] To date, organolead HPs (OHPs, A = methylammonium (MA), B = Pb, and X = I, Cl, or Br) have been successfully utilized as the active layer of memristor-based artificial synapses, emulat...

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The micro-alloying concepts have been widely used as a way to optimize the microstructure evolution and improve mechanical properties of conventional constructional steels. In the current study, the effect of micro-alloying on the properties of a multi-phase TRIP steel is analyzed. The micro-alloying of TRIP steel was found to lead to an increase of the yield stress rather than the tensile strength. A physical metallurgical explanation of the effect is proposed.