Texture Transfer in Dielectric Layers via Nanocrystalline Networks: Insights from in Situ 4D-STEM.

Transition metal oxide dielectric layers have emerged as promising candidates for various relevant applications, such as supercapacitors or memory applications. However, the performance and reliability of these devices can critically depend on their microstructure, which can be strongly influenced by thermal processing and substrate-induced strain. To gain a more in-depth understanding of the microstructural changes, we conducted in situ transmission electron microscopy (TEM) studies of amorphous HfO dielectric layers grown on highly textured (111) substrates.

Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks.

In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage () curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of s obtained from YO-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered.

Structural and Electrical Response of Emerging Memories Exposed to Heavy Ion Radiation.

Hafnium oxide- and GeSbTe-based functional layers are promising candidates in material systems for emerging memory technologies. They are also discussed as contenders for radiation-harsh environment applications. Testing the resilience against ion radiation is of high importance to identify materials that are feasible for future applications of emerging memory technologies like oxide-based, ferroelectric, and phase-change random-access memory.

Controlling the Formation of Conductive Pathways in Memristive Devices.

Resistive random-access memories are promising candidates for novel computer architectures such as in-memory computing, multilevel data storage, and neuromorphics. Their working principle is based on electrically stimulated materials changes that allow access to two (digital), multiple (multilevel), or quasi-continuous (analog) resistive states. However, the stochastic nature of forming and switching the conductive pathway involves complex atomistic defect configurations resulting in considerable variability.

Defect-Stabilized Substoichiometric Polymorphs of Hafnium Oxide with Semiconducting Properties.

Hafnium oxide plays an important role as a dielectric material in various thin-film electronic devices such as transistors and resistive or ferroelectric memory. The crystallographic and electronic structure of the hafnia layer often depends critically on its composition and defect structure. Here, we report two novel defect-stabilized polymorphs of substoichiometric HfO with semiconducting properties that are of particular interest for resistive switching digital or analog memory devices.