Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

In this work, we fabricated a Pt/SiN/TaN memristor device and characterized its resistive switching by controlling the compliance current and switching polarity. The chemical and material properties of SiN and TaN were investigated by X-ray photoelectron spectroscopy. Compared with the case of a high compliance current (5 mA), the resistive switching was more gradual in the set and reset processes when a low compliance current (1 mA) was applied by DC sweep and pulse train. In particular, low-power resistive switching was demonstrated in the first reset process, and was achieved by employing the negative differential resistance effect. Furthermore, conductance quantization was observed in the reset process upon decreasing the DC sweep speed. These results have the potential for multilevel cell (MLC) operation. Additionally, the conduction mechanism of the memristor device was investigated by I-V fitting.

Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to MLPs.

Robust quantum point contact operation of narrow graphene constrictions patterned by AFM cleavage lithography

Detecting conductance quantization in graphene nanostructures turned out more challenging than expected. The observation of well-defined conductance plateaus through graphene nanoconstrictions so far has only been accessible in the highest quality suspended or h-BN encapsulated devices. However, reaching low conductance quanta in zero magnetic field, is a delicate task even with such ultra-high mobility devices. Here, we demonstrate a simple AFM-based nanopatterning technique for defining graphene constrictions with high precision (down to 10 nm width) and reduced edge-roughness (+/−1 nm). The patterning process is based on the in-plane mechanical cleavage of graphene by the AFM tip, along its high symmetry crystallographic directions. As-defined, narrow graphene constrictions with improved edge quality enable an unprecedentedly robust QPC operation, allowing the observation of conductance quantization even on standard SiO2/Si substrates, down to low conductance quanta. Conductance plateaus, were observed at n × e2/h, evenly spaced by 2 × e2/h (corresponding to n = 3, 5, 7, 9, 11) in the absence of an external magnetic field, while spaced by e2/h (n = 1, 2, 3, 4, 5, 6) in 8 T magnetic field.