Ferroelectronics Lab

Understanding and utilizing non-volatile properties of materials

New Publication! “Perspective: Entropy-Stabilized Oxide Memristors”

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Abstract: A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability.

Full text available from Applied Physics Letters

New Publication! “Investigation of the Influence of Growth Conditions on the Local Structure in High Entropy Oxides Using S/TEM”

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Abstract: Recently, the chemically disordered multi-cation oxide class of materials called High Entropy Oxides (HEOs) has been widely explored due to their tunable functional properties because of their higher configurational entropy and to understand their fundamental phase formation. These HEOs have shown potential applications as thermoelectrics, ionic conductors, electrocaloric materials, etc. Since these systems have multiple aliovalent cations in a single lattice, it necessitates the understanding of elemental distribution and structure of these novel oxides. This would help us understand how the HEOs navigate the configurational space and enable us to establish a correlation between structure and property. In this study, we are investigating the influence of oxygen partial pressure during pulsed laser deposition on the structure, and chemical environment on the HEO thin films. (Mg0.2Ni0.2Co0.2Cu0.2Zn0.2)O, commonly known as ‘J14’, is the prototype HEO that has been widely studied. We are currently exploring the seven component HEO system with the composition: (Mg0.167Ni0.167Co0.167Cu0.167Zn0.167(Cr, Mn)0.167)O, referred to as ‘J14MnCr’. The goal of this study is to understand the influence of addition of Cr and Mn to J14 on the structure and how the growth conditions modulate the structure and tune magnetic properties. Due to the presence of seven cations with some cations that can adapt multiple valences, it is necessary to probe the structural nuances and chemical environment in these systems. In this study, Scanning/Transmission Electron Microscope is used to characterize the HEOs as S/TEM allows us to probe the structure, composition, and valence variation at nanoscale regime.

We have probed the J14MnCr thin film grown at 5mTorr and 50mTorr oxygen partial pressure. We have observed from Selected Area Electron Diffraction (SAED) and dark field TEM experiments that there is a change in the crystal structure in the films grown at 5mTorr vs. 50mTorr. At 50mTorr, the SAED reveals the formation of rock salt structure with additional secondary phase. The increase in oxygen partial pressure during synthesis has led to the nucleation of a new phase with a different structure which is speculated to influence the magnetic property. We have further performed STEM experiments and will be implementing unsupervised machine learning to detect the local structural variation. STEM-EELS experiments have also been performed to investigate the local changes in structure and to correlate it to changes in the valences of the cations and chemical environment. These S/TEM experiments enable us to understand the influence of processing conditions on microstructures and chemical environment, informing us about the structural nuances at the nanoscale and allowing us to tune the structure for desired properties.

Full text available from Microscopy and Microanalysis

New publication! “Achieving Semi-Metallic Conduction in Al-Rich AlGaN: Evidence of Mott Transition”

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Abstract: The development of high performance wide-bandgap AlGaN channel transistors with high current densities and reduced Ohmic losses necessitates extremely highly doped, high Al content AlGaN epilayers for regrown source/drain contact regions. In this work, we demonstrate the achievement of semi-metallic conductivity in silicon (Si) doped N-polar Al0.6Ga0.4N grown on C-face 4H-SiC substrates by molecular beam epitaxy. Under optimized conditions, the AlGaN epilayer shows smooth surface morphology and a narrow photoluminescence spectral linewidth, without the presence of any secondary peaks. A favorable growth window is identified wherein the free electron concentration reaches as high as ∼1.8 × 1020 cm−3 as obtained from Hall measurements, with a high mobility of 34 cm2/V·s, leading to a room temperature resistivity of only 1 mΩ·cm. Temperature-dependent Hall measurements show that the electron concentration, mobility, and sheet resistance do not depend on temperature, clearly indicating dopant Mott transition to a semi-metallic state, wherein the activation energy (Ea) falls to 0 meV at this high value of Si doping for the AlGaN films. This achievement of semi-metallic conductivity in Si doped N-polar high Al content AlGaN is instrumental for advancing ultrawide bandgap electronic and optoelectronic devices.

Full text available from Applied Physics Letters

New publication! “Efficient Data Processing Using Tunable Entropy-Stabilized Oxide Memristors“

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Abstract: Memristive devices are of potential use in a range of computing applications. However, many of these devices are based on amorphous materials, where systematic control of the switching dynamics is challenging. Here we report tunable and stable memristors based on an entropy-stabilized oxide. We use single-crystalline (Mg,Co,Ni,Cu,Zn)O films grown on an epitaxial bottom electrode. By adjusting the magnesium composition (XMg = 0.11–0.27) of the entropy-stabilized oxide films, a range of internal time constants (159–278 ns) for the switching process can be obtained. We use the memristors to create a reservoir computing network that classifies time-series input data and show that the reservoir computing system, which has tunable reservoirs, offers better classification accuracy and energy efficiency than previous reservoir system implementations.

Full text available from Nature Electronics

New Publication! “Nernst coefficient of Pt by non-local electrical measurement”

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Abstract: The Nernst effect describes a linear relationship between orthogonal components of a magnetic field, a temperature gradient, and a resulting transverse electric field. A non-local electrical measurement, where injection and detection are physically separated on the specimen, serves as a versatile and effective platform for measuring spin and thermal effects due to the avoided interference with a charge current directly. Here, we quantify the Nernst coefficient of Pt, a common material for spin injection in non-local geometries, by a non-local electrical measurement under modulated temperature and magnetic field and finite element analysis for modeling heat transfer. We determine the Nernst coefficient of Pt from room temperature (8.56 nVK-1 T-1) to 10K (29.3 nVK-1 T-1). Beyond the quantification of the Nernst coefficient, our results show that careful consideration of the thermal properties of the thermal sink and electrode materials is needed when making an interpretation of non-local electrical measurements.

Full text available from Applied Physics Letters

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