In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The ...Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti of ~500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (up to 10
), retention (≥10
s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes). We have experimentally shown that parylene-based memristive elements can be trained by a biologically inspired spike-timing-dependent plasticity (STDP) mechanism. The obtained results have been used to implement a simple neuromorphic network model of classical conditioning. The described advantages allow considering parylene-based organic memristors as prospective devices for hardware realization of spiking artificial neuron networks capable of supervised and unsupervised learning and suitable for biomedical applications.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving ...such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-
-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software ...realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)
(LiNbO
)
memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer ...critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer ...critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)sub.x(LiNbOsub.3)sub.100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.
Parylene is a widely used polymer possessing such advantages as low cost and safety for the human body. Recently, several studies have been conducted showing that parylene can be used as a dielectric ...layer of memristors — new circuit design elements that are promising for the implementation of hardware neural networks. However, the mechanism of resistive switching of parylene-based memristors remains unclear. In this paper, we report the result of a comprehensive study of this mechanism for Metal/Parylene/ITO sandwich memristive devices. The obtained results clearly show that the origin of resistive switching in the devices is the formation of conductive metal bridges (filaments) from the top electrode (Cu, Ag or Al) to the bottom one (ITO). And furthermore, conductance quantization effect with both integer and half-integer multiples of the quantum of conductance G0 = 2e2/h has been observed in the samples, which also confirms the chosen switching model, and can be useful in the development of multilevel data memory cells.
Display omitted
•Resistive switching in Metal/Parylene/ITO memristors is driven by the ECM mechanism.•Devices exhibit quantized conductance, including half-integer multiples of G0 = 2e2/h.•Parylene-based memristors are promising building blocks of neuromorphic systems.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software ...realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)
x
(LiNbO
3
)
100−
x
memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.
MLP-Mixer neuromorphic network based on nanocomposite memristive synapses has been developed for efficient and robust classification of images.
We present our last results on anomalous Hall effect (AHE) in (Co41Fe39B20)x(Al–O)100-x nanocomposites focusing on the possible correlation between temperature dependence of AHE and resistivity. It ...is shown that the temperature dependence of conductivity G=1/Rxx, where Rxx is resistivity, for compositions with x=49-56% at 10K<T<Tk follows the relation, where the parameters A, , Tk depend on x. For x=47% this relation changes to the exponential law “1/2” Rxx ∝ exp (Т0/T)1/2. The correlation between AHE resistivity RH (T) and resistivity Rxx (T) can be described as RH ∝ (Rxx)m , where m increases from 0.38 to 0.58 with an increase of x from 49 to 56 %.
Summary Background Viruses are the most dangerous infectious contaminants of human donor blood and blood products. The purpose of the study was to investigate the virus-inactivating properties of ...fullerene suspension regarding influenza virus in allantoic fluid of chicken embryos. Methods Influenza virus was propagated in chicken embryos, water suspension of C60 fullerene was added to the allantoic fluid. The fluid was light-irradiated at constant oxygen flow through the specimen, and the dynamics of virus titer was studied by virus titration in MDCK cells. The morphology of virions was studied by electron microscopy (EM). Results Dramatic drop of infectious titer (8 to 1 log 10 EID50) of the virus was observed within 2 h after start of irradiation. No change of the titers was observed in control specimens without fullerene, or light, or oxygen. EM study revealed numerous defects of virions’ morphology (destruction of outer membrane) leading to the loss of infectious properties of the virus. Conclusions Water-insoluble fullerenes may be considered as a prospective way for inactivation of enveloped viruses in biological materials including blood products.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
PDF
