Related papers: 3D Vertical Dual-Layer Oxide Memristive Devices fo…
The development of neuromorphic systems based on memristive elements - resistors with memory - requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of…
Ferroelectric domain walls are nanoscale objects that can be created, positioned, and erased on demand. They often embody functional properties that are distinct from the surrounding bulk material. Enhanced conductivity, for instance, is…
Three-dimensional interconnected nanowire networks have recently attracted notable attention for the fabrication of new devices for energy harvesting/storage, sensing, catalysis, magnetic and spintronic applications and for the design of…
The end of conventional Dennard scaling and the widening gap between memory bandwidth and arithmetic throughput have made the von Neumann partition a structural bottleneck rather than a transient one. Two-dimensional (2D) materials, with…
My research is dedicated to the electronic properties of functional oxides. My activity specifically focuses on ferroelectric tunnel junctions in which an ultrathin layer of ferroelectric material is intercalated between two metallic…
A new model of a symmetric two-terminal non-volatile RRAM device based on Perovskite oxide thin film materials, specifically Pr1-xCaxMnO3 (PCMO), is proposed and analyzed. The model consists of two identical half-parts, which are completely…
The recently emerged organohalide perovskites (e.g., CH3NH3PbI3) have drawn intense attention for high efficiency solar cells. However, with a considerable solubility in many solvents, these perovskites are not typically compatible with…
Superlattices may play an important role in next generation electronic and spintronic devices if the key-challenge of the reading and writing data can be solved. This challenge emerges from the coupling of low dimensional individual layers…
Scaling down materials to an atomic-layer level produces rich physical and chemical properties as exemplified in various two-dimensional (2D) crystals extending from graphene, transition metal dichalcogenides to black phosphorous. This is…
Replacing electrons with photons is a compelling route towards light-speed, highly parallel, and low-power artificial intelligence computing. Recently, all-optical diffractive neural deep neural networks have been demonstrated. However, the…
Heavy computational demands from artificial intelligence (AI) leads the research community to explore the design space for functional materials that can be used for high performance memory and neuromorphic computing hardware. Novel device…
Neuromorphic functionalities in memristive devices are commonly associated with the ability to electrically create local conductive pathways by resistive switching. The archetypal correlated material, VO2, has been intensively studied for…
Two dimensional (2D) crystal heterostructures are shown to possess a unique opportunity for novel THz nonlinear devices. In contrast to the oxide tunneling barrier, the uniformity of 2D insulators in the thickness control provides an ideal…
The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based volatile resistive switching memories (RRAMs) feature a…
Molecular-scale manipulation of electronic/ionic charge accumulation in materials is a preeminent challenge, particularly in electrochemical energy storage. Layered van der Waals (vdW) crystals exemplify a diverse family of materials that…
We demonstrate the feasibility of multilevel recording in Pt/Bi(1-d)FeO3/SrRuO3 capacitors using the ferroelectric resistive switching phenomenon exhibited by the Pt/Bi(1-d)FeO3 interface. A tunable population of up and down ferroelectric…
Two-dimensional (2D) semiconductors are promising channel materials for continued downscaling of complementary metal-oxide-semiconductor (CMOS) logic circuits. However, their full potential continues to be limited by a lack of scalable…
We present a combined experimental and theoretical study to get insight into both memory and negative differential resistance (NDR) effect in organic memory devices. The theoretical model we propose is simply a one-dimensional metallic…
While most neuromorphic systems are based on nanoscale electronic devices, nature relies on ions for energy-efficient information processing. Therefore, finding memristive nanofluidic devices is a milestone toward realizing electrolytic…
We present a fast generative modeling approach for resistive memories that reproduces the complex statistical properties of real-world devices. To enable efficient modeling of analog circuits, the model is implemented in Verilog-A. By…