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 HfO2 dielectric layers grown on highly textured (111) substrates. Our results indicate that the minimum required phase transition temperature is 180 °C and that the developed crystallinity is affected by texture transfer. Using in situ TEM and 4D-STEM can provide valuable insights into the fundamental mechanisms underlying the microstructural evolution of dielectric layers and could pave the way for the development of more reliable and efficient devices for future applications.

Crystal and Electronic Structure of Oxygen Vacancy Stabilized Rhombohedral Hafnium Oxide.

Hafnium oxide is an outstanding candidate for next-generation nonvolatile memory solutions such as OxRAM (oxide-based resistive memory) and FeRAM (ferroelectric random access memory). A key parameter for OxRAM is the controlled oxygen deficiency in HfO2-x which eventually is associated with structural changes. Here, we expand the view on the recently identified (semi-)conducting low-temperature pseudocubic phase of reduced hafnium oxide by further X-ray diffraction analysis and density functional theory (DFT) simulation and reveal its rhombohedral nature. By performing total energy and electronic structure calculations, we investigate phase stability and band structure modifications in the presence of oxygen vacancies. With increasing oxygen vacancy concentration, the material transforms from the well-known monoclinic structure to a (pseudocubic) polar rhombohedral r-HfO2-x structure. The DFT analysis shows that r-HfO2-x is not merely epitaxy-induced but may exist as a relaxed compound. Furthermore, the electronic structure of r-HfO2-x as determined by X-ray photoelectron spectroscopy and UV/Vis spectroscopy corresponds very well with the DFT-based prediction of a conducting defect band. The existence of a substoichiometric (semi-)conducting phase of HfO2-x is obviously an important ingredient to understand the mechanism of resistive switching in hafnium-oxide-based OxRAM.

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 (I-V) 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 I-Vs obtained from Y2O3-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required.

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. Induced changes of the crystalline and microscopic structure have to be considered as they are directly related to the memory states and failure mechanisms of the emerging memory technologies. Therefore, we present heavy ion irradiation-induced effects in emerging memories based on different memory materials, in particular, HfO2-, HfZrO2-, as well as GeSbTe-based thin films. This study reveals that the initial crystallinity, composition, and microstructure of the memory materials have a fundamental influence on their interaction with Au swift heavy ions. With this, we provide a test protocol for irradiation experiments of hafnium oxide- and GeSbTe-based emerging memories, combining structural investigations by X-ray diffraction on a macroscopic, scanning transmission electron microscopy on a microscopic scale, and electrical characterization of real devices. Such fundamental studies can be also of importance for future applications, considering the transition of digital to analog memories with a multitude of resistance states.

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. This paper reveals that the intricate interplay of 0D and 2D defects can be engineered to achieve reproducible and controlled low-voltage formation of conducting filaments. The author find that the orientation of grain boundaries in polycrystalline HfOx is directly related to the required forming voltage of the conducting filaments, unravelling a neglected origin of variability. Based on the realistic atomic structure of grain boundaries obtained from ultra-high resolution imaging combined with first-principles calculations including local strain, this paper shows how oxygen vacancy segregation energies and the associated electronic states in the vicinity of the Fermi level govern the formation of conductive pathways in memristive devices. These findings are applicable to non-amorphous valence change filamentary type memristive device. The results demonstrate that a fundamental atomistic understanding of defect chemistry is pivotal to design memristors as key element of future electronics.

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 HfO2-x with semiconducting properties that are of particular interest for resistive switching digital or analog memory devices. The thin-film samples are synthesized by molecular beam epitaxy with oxygen engineering that allows us to cover the whole range of metallic Hf with oxygen interstitials to HfO2. The crystal and defect structures, in particular of a cubic low-temperature phase c-HfO1.7 and a hexagonal phase hcp-HfO0.7 are identified by X-ray diffraction, in vacuo electron spectroscopic, and transmission electron microscopic methods. With the help of UV/Vis transmission data, we propose a consistent band structure model for the whole oxidation range involving oxygen vacancy-induced in-gap defect states. Our comprehensive study of engineered hafnia thin films has an impact on the design of resistive memory devices and can be transferred to chemically similar suboxide systems.