Sundar Kunwar

Functional oxide thin films are promising candidates for high-temperature applications due to their thermal stability and tunable defect chemistry. In this work, we investigate strontium-doped samarium oxide thin films. While the bulk synthesis of the equimolar ceramic compound has been previously reported,^[1] to our knowledge the work herein is the first demonstration of this material fabricated as a thin film and the first investigation of its electrochemical properties.

Interface-type (IT) memristors show great promise as next-generation memory for neuromorphic computing. The IT memristors based on Au/Nb:STO are firstly fabricated and thoroughly tested, and the characterization is presented in this paper. Like other memristors, these IT memristors endure challenges such as low ON/OFF current ratio and high non-linearity, thereby reducing the inference accuracy and limiting their potential application in neuromorphic systems. To address these issues, the following optimization techniques are proposed: (i) Parallel connection of IT memristors to increase number of levels in composite device and suppress spatial variations; and (ii) Implementing a non-linear optimization to mitigate the inherent non-linearity, which causes inaccurate weight updates. Simulation results indicate that the proposed optimization approaches lead to a great improvement in the inference accuracy of the neuromorphic system: up to ~18% is observed in 2-segment models, and up to ~59% in 3-segment models. Connecting memristors in parallel effectively alleviated the impact of spatial variations, achieving accuracy of ~86% on MNIST dataset using a 2-layer fully connected neural network. Despite these advancements, the proposed optimization methods lead to increased energy consumption and consequently larger carbon footprint, thereby revealing an important trade-off between accuracy, overall performance, and carbon footprint. A linear increase in the carbon footprint has been observed with an increase in the number of parallel memristors, while 2-segment models have the lowest carbon footprint. Through a comprehensive analysis, the simulation results highlight the potential of IT memristors and optimization methods for advanced neuromorphic architectures.

The U.S. Department of Energy's Office of Scientific and Technical Information

Developing novel lead‐free ferroelectric materials is crucial for next‐generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time‐consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high‐throughput combinatorial synthesis approach to fabricate lead‐free ferroelectric superlattices and solid solutions of (Ba_0.7Ca_0.3)TiO₃ (BCT) and Ba(Zr_0.2Ti_0.8)O₃ (BZT) phases with continuous variation of composition and layer thickness. High‐resolution x‐ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well‐controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT}_N superlattice geometry. This high‐throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth. image

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO₂/La_0.7Sr_0.3MnO₃ (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (V_o) at the VO₂/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal–insulator transition (MIT) of VO₂ under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector–one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

As the energy demand is expected to double over the next 30 years, there has been a major initiative towards advancing the technology of both energy harvesting and storage for renewable energy. In this work, we explore a subset class of dielectrics for energy storage since ferroelectrics offer a unique combination of characteristics needed for energy storage devices. We investigate ferroelectric lead-free 0.5[Ba(Ti_0.8Zr_0.2)O₃]-0.5(Ba_0.7Ca_0.3)TiO₃ epitaxial thin films with different crystallographic orientations grown by pulsed laser deposition. We focus our attention on the influence of the crystallographic orientation on the microstructure, ferroelectric, and dielectric properties. Our results indicate an enhancement of the polarization and strong anisotropy in the dielectric response for the (001)-oriented film. The enhanced ferroelectric, energy storage, and dielectric properties of the (001)-oriented film is explained by the coexistence of orthorhombic-tetragonal phase, where the disordered local structure is in its free energy minimum.