Search

Hafnium zirconium oxide (HZO) has been studied intensively due to its potential application to memory and logic devices based on its outstanding dielectric and ferroelectric properties. Because many process factors can significantly impact the properties of HZO thin film, an optimized process or post‐treatment is necessary to ensure its performance. Herein, the effect of hydrogen on HZO thin film can make the film degraded. Analysis of the polarization–voltage hysteresis loops and I–V measurements indicate a leaky behavior in the film. To address this issue, post‐oxygen‐plasma treatment is performed on the film, which results in the decrease in hydrogen ions inside the film and consequently reduces the leakage current density. This finding can provide new insight into the effect of hydrogen incorporation for HZO thin films and methodology to remove leaky components inside ferroelectric films using O2 plasma treatment. [ABSTRACT FROM AUTHOR]

A highly scalable synapse device based on a junctionless (JL) ferroelectric (FE) FinFET is presented for neuromorphic applications. The synaptic behaviors of the JL metal-ferroelectric-insulator-silicon FinFET were experimentally demonstrated after verifying the ferroelectric characteristics of the HfZrOX (HZO) film using a metal-ferroelectric-metal capacitor. The fabricated synapse showed distinguishable polarization switching behaviors with gradually controllable channel conductance. From neural network simulations using the proposed JL FE FinFET as synapses, the pattern recognition accuracy for hand-written digits was validated to be approximately 80% for neuromorphic applications. [ABSTRACT FROM AUTHOR]

Nanostructured electrodes and their flexible integrated systems have great potential for many applications, including electrochemical energy storage, electrocatalysis and solid-state memory devices, given their ability to improve faradaic reaction sites by large surface area. Although many processing techniques have been employed to fabricate nanostructured electrodes onto flexible substrates, these present limitations in terms of achieving flexible electrodes with high mechanical stability. In this study, the adhesion, mechanical properties and flexibility of TiN nanotube arrays on a Pt substrate were improved using a Ti interlayer. Highly ordered and well-aligned TiN nanotube arrays were fabricated on a Pt substrate using a template-assisted method with an anodic aluminum oxide (AAO) template and atomic layer deposition (ALD) system. We show that with the use of a Ti interlayer between the TiN nanotube arrays and Pt substrate, the TiN nanotube arrays could perfectly attach to the Pt substrate without delamination and faceted phenomena. Furthermore, the I-V curve measurements confirmed that the electric contact between the TiN nanotube arrays and substrate for use as an electrode was excellent, and its flexibility was also good for use in flexible electronic devices. Future efforts will be directed toward the fabrication of embedded electrodes in flexible plastic substrates by employing the concepts demonstrated in this study. [ABSTRACT FROM AUTHOR]

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.