The confluence of Big Data, IoT and Real-Time Analytics calls for rethinking of the hardware computing paradigm, either by the bottom up or top down approach. Memristive neuromorphic systems inspired by brain functions and implemented through new materials properties, bionic memristive devices (e.g., artificial synapses and neurons) and neural network circuits, are emerging with the promise of transforming the semiconductor information processing technology.Since the experimental discovery of memristors twelve years ago, memristive neuromorphic hardware has continued to make big strides. On the level of the material, a huge number of materials of various categories have shown memristive properties and this number continues to increase rapidly. In such a growing filed, there is a crucial need to establish materials selection rules and evaluate the suitability of the corresponding devices for neuromorphic systems. Device yield testing, performance distribution analysis and circuit reliability simulations have to be performed and standardized to turn these materials research endeavors into real impact. On the device level, researchers have showcased devices with diverse physical mechanisms, primary neuromorphic functions, and intriguing performances. Highly reproducible devices with desired characteristics, and versatile devices integrating multiple neural dynamics and computational capabilities remain to be demonstrated to further boost the system performance and functionali...

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The Biology of Marvel’s Symbiotes

Charge density wave (CDW) is a quantum mechanical phenomenon, which induces distortion in the crystal structures of some low-dimensional (1D or 2D) metals, when the temperature is reduced. Such distorted crystal structure is known as CDW phase and its resistivity is much higher than the original symmetric phase. Since the switching between symmetric and CDW phase can also be made by the application of external electric field, these materials are technologically important and have attracted immense attention in the nanoelectronics community.

Novel technique could lead to tiny, self-powered devices for environmental, industrial, or medical monitoring.

Scientific Reports - Avalanche criticality in LaAlO $$_3$$ 3 and the effect of aspect ratio

Congratulations to Lyes Khacef for receiving the first thesis prize from the STIC doctoral school in Université Côte d'Azur! We are happy to have you now at…

PDF | Video sentiment analysis can effectively establish the relationship between the emotion state and the multimodal information, while still suffer... | Find, read and cite all the research you need on ResearchGate

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Scientific Reports - Enhancing membrane-based soft materials with magnetic reconfiguration events

IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step

Curt A. Richter National Institute of Standards and Technology

ETH Zuerich

Politecnico di Milano (POLIMI)

How manganite-based Memristor Behavior Impacts Faster Learning in Hardware Neural Networks In a groundbreaking study focused on the emerging field of neuromorphic computing, MELON researchers from CONICET-centre, Argentina, have shed new light on the potential of oxide-based memristor arrays with

Machine learning meeting physics: everything should be as simple as possible, but not simpler Dimensionality reduction methods such as principal component…

A memristor is a two-terminal device that undergoes a voltage-controlled conductance change. Because the resistance depends on the history of the system, it has a strong hysteresis effect and produces a resistance switching. Memristors are the key elements for neuronal networks, as the memory effect represents plasticity of synapses. Neurons have the same ingredients as memristors plus at least one negative resistance that destabilizes the system in a Hopf bifurcation that passes the dynamics from rest to a spiking state. The operation of spiking networks occurs by transference and integration of electrical impulses, but the characterization of the material elements is much better done in the frequency domain, by the techniques of impedance spectroscopy.1 Here we provide the methods to assess memory, plasticity and spiking in the frequency domain, and we show the transformation to the time domain. We present the fundamental model of a halide perovskite memristor, that describes the behaviour both in time and frequency domain.2 Next, we show the impedance spectroscopy criteria for dynamical regimes of a FitzHugh-Nagumo model, that is a representative minimal model of a spiking neuron.3 We expand the analysis to cover the possible impedance spectroscopy behaviours of all two-dimensional oscillating systems. In conclusion we show that impedance spectroscopy is a strong characterization method for producing memristors, synapses and neurons with tailored temporal dynamics, hysteresis, and rhythmic oscillations for neuromorphic computing. (1) Guerrero, A.; Bisquert, J.; Garcia-Belmonte, G. Impedance spectroscopy of metal halide perovskite solar cells from the perspective of equivalent circuits, Chemical Reviews 2021, 121, 14430–14484. (2) Bou, A.; Bisquert, J. Impedance spectroscopy dynamics of biological neural elements: from memristors to neurons and synapses, J. Phys. Chem. B 2021, 125 9934–9949. (3) Bisquert, J. A frequency domain analysis of excitability and bifurcations of Fitzhugh-Nagumo neuron model., J. Phys. Chem. Lett. 2021, 12, 11005–11013.

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