Organic-inorganic halide perovskite (OHP) materials, for example, CH NH PbI (MAPbI ), have attracted significant interest for applications such as solar cells, photodectors, light-emitting diodes, and lasers. Previous studies have shown that charged defects can migrate in perovskites under an electric field and/or light illumination, potentially preventing these devices from practical applications. Understanding and control of the defect generation and movement will not only lead to more stable devices but also new device concepts. Here, it is shown that the formation/annihilation of iodine vacancies (V 's) in MAPbI films, driven by electric fields and light illumination, can induce pronounced resistive switching effects. Due to a low diffusion energy barrier (≈0.17 eV), the V 's can readily drift under an electric field, and spontaneously diffuse with a concentration gradient. It is shown that the V diffusion process can be suppressed by controlling the affinity of the contact electrode material to I ions, or by light illumination. An electrical-write and optical-erase memory element is further demonstrated by coupling ion migration with electric fields and light illumination. These results provide guidance toward improved stability and performance of perovskite-based optoelectronic systems, and can lead to the development of solid-state devices that couple ionics, electronics, and optics.

No abstract

Rapid advances in the semiconductor industry, driven largely by device scaling, are now approaching fundamental physical limits and face severe power, performance, and cost constraints. Multifunctional materials and devices may lead to a paradigm shift toward new, intelligent, and efficient computing systems, and are being extensively studied. Herein examines how, by controlling the internal ion distribution in a solid‐state film, a material's chemical composition and physical properties can be reversibly reconfigured using an applied electric field, at room temperature and after device fabrication. Reconfigurability is observed in a wide range of materials, including commonly used dielectric films, and has led to the development of new device concepts such as resistive random‐access memory. Physical reconfigurability further allows memory and logic operations to be merged in the same device for efficient in‐memory computing and neuromorphic computing systems. By directly changing the chemical composition of the material, coupled electrical, optical, and magnetic effects can also be obtained. A survey of recent fundamental material and device studies that reveal the dynamic ionic processes is included, along with discussions on systematic modeling efforts, device and material challenges, and future research directions.

Memristors, based on inherent memory effects in simple two-terminal structures, have attracted tremendous interest recently for applications ranging from nonvolatile data storage to neuromorphic computing based on non-von Neumann architectures. In a memristor, the ability to modulate and retain the state of an internal variable leads to experimentally observed resistive switching (RS) effects. Such phenomena originate from internal, microscopic ionic migration and associated electrochemical processes that modify the materials' electrical and other physical properties. To optimize the device performance for practical applications with large-size arrays, controlling the internal ionic transport and redox reaction processes thus becomes a necessity, ideally at the atomic scale. Here we show that the RS characteristics in tantalum-oxide-based memristors can be systematically tuned by inserting a graphene film with engineered nanopores. Graphene, with its atomic thickness and excellent impermeability and chemical stability, can be effectively integrated into the device stack and can offer unprecedented capabilities for the control of ionic dynamics at the nanoscale. In this device structure, the graphene film effectively blocks ionic transport and redox reactions; thereby the oxygen vacancies required during the RS process are allowed to transport only through the engineered nanosized openings in the graphene layer, leading to effective modulation of the device performance by controlling the nanopore size in graphene. The roles of graphene as an ion-blocking layer in the device structure were further supported by transmission electron microscopy, energy-dispersive X-ray spectroscopy, and atomistic simulations based on first-principles calculations.

Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the most commonly used feature extraction techniques, through online, unsupervised learning. Using Sanger's rule, that is, the generalized Hebbian algorithm, the principal components were obtained as the memristor conductances in the network after training. The network was then used to analyze sensory data from a standard breast cancer screening database with high classification success rate (97.1%).