Transition-metal oxides exhibiting a bistable resistance state are attractive for non-volatile memory applications. The relevance of oxygen vacancies (VO) for the resistance-change memory was investigated with x-ray fluorescence, infrared microscopy, and x-ray absorption spectroscopy using Cr-doped SrTiO3 as example. We propose that the microscopic origin of resistance switching in this class of materials is due to an oxygen-vacancy drift occurring in close proximity to one of the electrodes.

Role of oxygen vacancies in Cr-doped SrTiO3 for resistance-change memory

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

https://iopscience.iop.org/article/10.1088/1361-6463/ab2e9e/pdf

Advances in resistive switching based memory devices

While the physics of the Schottky barrier is relatively well understood, much less is known about the band alignment at the insulator/insulator interface. As a model problem we study theoretically the band alignment at the technologically important $\\mathrm{Si}{\\mathrm{O}}_{2}∕\\mathrm{Hf}{\\mathrm{O}}_{2}$ interface using density functional theory. We report several different atomic level models of this interface along with their energies and electronic properties. We find that the valence band offset increases near linearly with the interfacial oxygen coordination, changing from $\\ensuremath{-}2.0\\phantom{\\rule{0.3em}{0ex}}\\mathrm{eV}\\phantom{\\rule{0.3em}{0ex}}\\text{to}\\phantom{\\rule{0.3em}{0ex}}1.0\\phantom{\\rule{0.3em}{0ex}}\\mathrm{eV}$. For the fully oxidized interface the Schottky limit is reached. We propose a simple model, which relates the screening properties of the interfacial layer to the band offset. Our results may explain a somewhat confusing picture provided by recent experiments.

/pdf/theoretical-study-of-the-insulator-insulator-interface-band-1ianjhsrui.pdf

Theoretical study of the insulator/insulator interface: band alignment at the SiO$_{2}$/HfO$_{2}$ junction

Ferroelectric structural transition in hafnium oxide induced by charged oxygen vacancies

In this article, we analyzed the experimental data based on the TaOx memristor and found that the threshold switching (TS) characteristics are related to temperature, and its logarithmic I–V curve is in good agreement with the space charge limiting current conduction mechanism. We use this mechanism to establish a TS physical model and then use the physical model to build an LTspice model. The model data are fitted with the experimental data, which is basically consistent. Next, using the TS memristor to simulate a leaky integrate-and-fire neuron circuit, the basic dynamics are realized. By changing the external temperature of the memristor, the output frequency of the neuron will be more intense as the temperature increases. Finally, an artificial spiking neural network (SNN) was built based on this neuron circuit for MNIST recognition task. In this SNN, the input signals fused both voltage amplitude and temperature to achieve neuromorphic multimodal preprocessing and enhance the recognition accuracy. These results demonstrated the reliability of the model, which enhanced the flexibility for exploring the application of TaOx-based TS memristors.

A model of TaOx threshold switching memristor for neuromorphic computing

Using hybrid density functional calculations, this paper systematically investigated the common defects in m-HfO2 and β-Si3N4 trapping layer, respectively, on the performance of charge trapping memory (CTM) devices. Defects may act as charge traps or fixed charges, however, only traps can be used to store charges. Thus, firstly, the paper distinguished traps or fixed charges of defects in trapping layer through the band alignment method. Then we can restrain the formation of defects that act as fixed charges in manufacturing CTM devices, which is very meaningful in improving the performance of CTM. Secondly, it studied the storage performance of traps in m-HfO2 and β-Si3N4 by analyzing the formation energy, the atomistic structural change during program/erase (P/E) cycles, and localization energy. The simulation results found that only some defects can be charge traps in m-HfO2 and β-Si3N4, and the charge transition of these traps only appeared between certain charged states. In addition, these traps show reversible structural change during P/E cycles and different localizing abilities for holes and electrons. Therefore, this study would provide some theoretical guidance for improving the performance of CTM.

Impact of native defects and impurities in m−HfO2 and β−Si3N4 on charge trapping memory devices: A first principle hybrid functional study

With the growing application of high-k dielectrics, the interface between HfO2 and Al2O3 play a crucial role in CTM devices. To clearly understand the interaction of the HfO-AlO interface at the atomic and electronic scale, the bonding feature, electronic properties and charge localized character of c- HfO2 (0 0 1)/α-Al2O3 (1 -1 0 2) interface has been investigated by first principle calculations. The c- HfO2 (0 0 1)/α-Al2O3 (1 -1 0 2) interface has adhesive energy about -1.754 J/m2, suggesting that this interface can exist stably. Through analysis of Bader charge and charge density difference, the intrinsic interfacial gap states are mainly originated from the OII and OIII types oxygen atoms at the interface, and only OIII type oxygen atoms can localized electrons effectively and are provided with good reliability during P/E cycles, which theoretically validate the experimental results that HfO2/Al2O3 multi-layered charge trapping layer can generate more effective traps in memory device. Furthermore, the...

/pdf/research-on-c-hfo2-0-0-1-a-al2o3-1-1-0-2-interface-in-ctm-4glc8mp7t4.pdf

Research on c-HfO2 (0 0 1)/α-Al2O3 (1 -1 0 2) interface in CTM devices based on first principle theory

Six different interfaces namely, armchair Graphene (aGNR), zigzag Graphene (zGNR), and surface defect zigzag Graphene (zGNR1) nanoribbons with uni- and bi-laminar -oriented NiO were studied. First, the Mulliken mean and difference populations, the interface energy, and the interface adhesion energy were calculated by the Cambridge sequential total energy package (CASTEP). The aGNR/NiO interface showed higher interface adhesion energy and Mulliken population mean as compared to the other interface structures (i.e., aGNR/NiO was more compact than the rest of interfaces). Moreover, the lowest interface energy and Mulliken difference population values along with the negligible aberration state clearly revealed aGNR/NiO to be the best interface among those studied herein. Subsequently, the current–voltage (I–V) curves indicate the aGNR/NiO/aGNR device presents memory effect while tracing the path back in the current data, but not switching between positive and negative voltages due to the device unipolar behavior. The mechanism of resistive switching is demonstrated by performing density functional tight binding and much more (DFTB+) dynamics.

The study about the resistive switching based on graphene/NiO interfaces

The physical mechanism of the resistance switching for RRAM with co-doped defects (Ag and oxygen vacancy) is studied based on the first principle calculations and the simulation tool VASP. The interaction energy, formation energy and density of states of Ag and oxygen vacancy defect (VO) are calculated. The calculated results reveal that the co-doped system is more stable than the system only doped either Ag or VO defect and the impurity energy levels in the band gap are contributed by Ag and VO defects. The obtained partial charge density confirmed further that the clusters are obvious in the direction of Ag to Hf ions, which means that it is Ag but VO plays a role of conductive paths. For the formation mechanism, the modified electron affinity and the partial charge density difference are calculated. The results show that the ability of electron donors of Ag is stronger than Vm O In conclusion, the conductivity of the physical mechanism of resistance switching in the co-doped system mainly depends on the doped Ag.

http://iopscience.iop.org/article/10.1088/1674-4926/38/1/014008/pdf

Physical mechanism of resistance switching in the co-doped RRAM

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