Transient Structures and Possible Limits of Data Recording in Phase-Change Materials

Phase-change materials (PCMs) represent the leading candidates for universal data storage devices, which exploit the large difference in the physical properties of their transitional lattice structures. On a nanoscale, it is fundamental to determine their performance, which is ultimately controlled by the speed limit of transformation among the different structures involved. Here, we report observation with atomic-scale resolution of transient structures of nanofilms of crystalline germanium telluride, a prototypical PCM, using ultrafast electron crystallography. A nonthermal transformation from the initial rhombohedral phase to the cubic structure was found to occur in 12 ps. On a much longer time scale, hundreds of picoseconds, equilibrium heating of the nanofilm is reached, driving the system toward amorphization, provided that high excitation energy is invoked. These results elucidate the elementary steps defining the structural pathway in the transformation of crystalline-to-amorphous phase transitions and describe the essential atomic motions involved when driven by an ultrafast excitation. The establishment of the time scales of the different transient structures, as reported here, permits determination of the possible limit of performance, which is crucial for high-speed recording applications of PCMs

On the self-consistency of DFT-1/2

DFT-1/2 is an efficient band gap rectification method for density functional theory (DFT) under local density approximation (LDA) or generalized gradient approximation. It was suggested that non-self-consistent DFT-1/2 should be used for highly ionic insulators like LiF, while self-consistent DFT-1/2 should still be used for other compounds. Nevertheless, there is no quantitative criterion prescribed for which implementation should work for an arbitrary insulator, which leads to severe ambiguity in this method. In this work we analyze the impact of self-consistency in DFT-1/2 and shell DFT-1/2 calculations in insulators or semiconductors with ionic bonds, covalent bonds and intermediate cases, and show that self-consistency is required even for highly ionic insulators for globally better electronic structure details. The self-energy correction renders electrons more localized around the anions in self-consistent LDA-1/2. The well-known delocalization error of LDA is rectified, but with strong overcorrection due to the presence of additional self-energy potential. However, in non-self-consistent LDA-1/2 calculations, the electron wavefunctions indicate that such localization is much more severe and beyond a reasonable range, because the strong Coulomb repulsion is not counted in the Hamiltonian. Another common drawback of non-self-consistent LDA-1/2 lies in that the ionicity of the bonding gets substantially enhanced, and the band gap can be enormously high in mixed ionic-covalent compounds like $\mathrm{TiO_2}$. The impact of LDA-1/2-induced stress is also discussed comprehensively.Comment: 31 pages, 16 figure

Impact of Zr substitution on the electronic structure of ferroelectric hafnia

$\mathrm{HfO_2}$-based dielectrics are promising for nanoscale ferroelectric applications, and the most favorable material within the family is Zr-substituted hafnia, i.e., $\mathrm{Hf_{1-x}Zr_xO_2}$ (HZO). The extent of Zr substitution can be great, and x is commonly set to 0.5. However, the band gap of $\mathrm{ZrO_2}$ is lower than $\mathrm{HfO_2}$, thus it is uncertain how the Zr content should influence the electronic band structure of HZO. A reduced band gap is detrimental to the cycling endurance as charge injection and dielectric breakdown would become easier. Another issue is regarding the comparison on the band gaps between $\mathrm{HfO_2}$/$\mathrm{ZrO_2}$ superlattices and HZO solid-state solutions. In this work we systematically investigated the electronic structures of $\mathrm{HfO_2}$, $\mathrm{ZrO_2}$ and HZO using self-energy corrected density functional theory. In particular, the conduction band minimum of $Pca2_1$-$\mathrm{HfO_2}$ is found to lie at an ordinary k-point on the Brillouin zone border, not related to any interlines between high-symmetry k-points. Moreover, the rule of HZO band gap variation with respect to x has been extracted. The physical mechanisms for the exponential reduction regime and linear decay regime have been revealed. The band gaps of $\mathrm{HfO_2}$/$\mathrm{ZrO_2}$ ferroelectric superlattices are investigated in a systematic manner, and the reason why the superlattice could possess a band gap lower than that of $\mathrm{ZrO_2}$ is revealed through comprehensive analysis.Comment: 23 pages, 9 figure

Isolating hydrogen in hexagonal boron nitride bubbles by a plasma treatment

Atomically thin hexagonal boron nitride (h-BN) is often regarded as an elastic film that is impermeable to gases. The high stabilities in thermal and chemical properties allow h-BN to serve as a gas barrier under extreme conditions.In this work, we demonstrate the isolation of hydrogen in bubbles of h-BN via plasma treatment.Detailed characterizations reveal that the substrates do not show chemical change after treatment. The bubbles are found to withstand thermal treatment in air,even at 800 degree celsius. Scanning transmission electron microscopy investigation shows that the h-BN multilayer has a unique aligned porous stacking nature, which is essential for the character of being transparent to atomic hydrogen but impermeable to hydrogen molecules. We successfully demonstrated the extraction of hydrogen gases from gaseous compounds or mixtures containing hydrogen element. The successful production of hydrogen bubbles on h-BN flakes has potential for further application in nano/micro-electromechanical systems and hydrogen storage.Comment: 55 pages, 33figure

Terahertz nanoimaging and nanospectroscopy of chalcogenide phase-change materials

Chalcogenide phase-change materials (PCMs) exhibit optical phonons at terahertz (THz) frequencies, which can be used for studying basic properties of the phase transition and which lead to a strong dielectric contrast that could be exploited for THz photonics applications. Here, we demonstrate that the phonons of PCMs can be studied by frequency-tunable THz scattering-type scanning near-field optical microscopy (s-SNOM). Specifically, we perform spectroscopic THz nanoimaging of a PCM sample comprising amorphous and crystalline phases. We observe phonon signatures, yielding strong s-SNOM signals and, most important, clear spectral differences between the amorphous and crystalline PCM, which allows for distinguishing the PCM phases with high confidence on the nanoscale. We also found that the spectral signature can be enhanced, regarding both signal strength and spectral contrast, by increasing the radius of the probing tip. From a general perspective, our results establish THz s-SNOM for nanoscale structural and chemical mapping based on local phonon spectroscopy.C.C. acknowledges the Postdoctoral Fund of Hubei Province (Grant No. 182/0106182067). M.X. acknowledges the National Key R&D Plan of China (Grant No. 2017YFB0701701 “Materials Genome Engineering”) and the National Natural Science Foundation of China (Grant No. 51772113). M.L. and T.T. acknowledge funding from the DFG (German Science Foundation) within the collaborative research center SFB 917 “Nanoswitches”. P. L. acknowledges the National Natural Science Foundation of China (Grant No. 62075070). R.H. acknowledges financial support from the European Union’s H2020 FET OPEN Project PETER (GA#767227), the Spanish Ministry of Science, Innovation and Universities (National Project RTI2018-094830-B-100 and the Project MDM-2016-0618 of the Marie de Maeztu Units of Excellence Program), and the Basque Government (Grant No. IT1164-19).Peer reviewe

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Biologically plausible computing systems require fine- grain tuning of analog synaptic characteristics. In this study, lithium- doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state- dependent decay to be reliably achieved. As a result, this device offers multi- bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short- term memory and long- term memory are emulated across dynamical timescales. Spike- timing- dependent plasticity and paired- pulse facilitation are also demonstrated. These mechanisms are capable of self- pruning to generate efficient neural networks. Time- dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human’s higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.In this study, lithium- doped silicate resistive random access memory with a titanium nitride (TiN) electrode is shown to mimic biological synapses. The TiN electrode effectively stores lithium ions, a principle widely adopted from battery construction, and enables reliable state- dependent decay. This device offers multi- bit functionality and synaptic plasticity, short- term memory and long- term memory, spike- timing- dependent plasticity and paired- pulse facilitation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/3/smll202003964-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/2/smll202003964_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/1/smll202003964.pd

Manipulation of dangling bonds of interfacial states coupled in GeTe-rich GeTe/Sb2Te3 superlattices

Abstract Superlattices consisting of stacked nano-sized GeTe and Sb2Te3 blocks have attracted considerable attention owing to their potential for an efficient non-melting switching mechanism, associated with complex bonding between blocks. Here, we propose possible atomic models for the superlattices, characterized by different interfacial bonding types. Based on interplanar distances extracted from ab initio calculations and electron diffraction measurements, we reveal possible intercalation of dangling bonds as the GeTe content in the superlattice increases. The dangling bonds were further confirmed by X-ray photoelectron spectroscopy, anisotropic temperature dependent resistivity measurements down to 2 K and magnetotransport analysis. Changes of partially coherent decoupled topological surfaces states upon dangling bonds varying contributed to the switching mechanism. Furthermore, the topological surface states controlled by changing the bonding between stacking blocks may be optimized for multi-functional applications

Bring memristive in-memory computing into general-purpose machine learning: A perspective

In-memory computing (IMC) using emerging nonvolatile devices has received considerable attention due to its great potential for accelerating artificial neural networks and machine learning tasks. As the basic concept and operation modes of IMC are now well established, there is growing interest in employing its wide and general application. In this perspective, the path that leads memristive IMC to general-purpose machine learning is discussed in detail. First, we reviewed the development timeline of machine learning algorithms that employ memristive devices, such as resistive random-access memory and phase-change memory. Then we summarized two typical aspects of realizing IMC-based general-purpose machine learning. One involves a heterogeneous computing system for algorithmic completeness. The other is to obtain the configurable precision techniques for the compromise of the precision-efficiency dilemma. Finally, the major directions and challenges of memristive IMC-based general-purpose machine learning are proposed from a cross-level design perspective