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Volatile threshold resistive switching and neuronal oscillations in phase-change materials, specifically those undergoing metal-to-insulator transitions, offer unique attributes such as fast and low-field volatile switching, tunability, and…
Artificial neuronal devices are the basic building blocks for neuromorphic computing systems, which have been motivated by realistic brain emulation. Aiming for these applications, various device concepts have been proposed to mimic the…
With remarkable electrical and optical switching properties induced at low power and near room temperature (68C), vanadium dioxide (VO2) has sparked rising interest in unconventional computing among the phase-change materials research…
Brain-inspired non-Boolean computing offers intrinsic error tolerance and parallelism, but its practical deployment is limited by the lack of compact, energy-efficient spiking hardware compatible with large-scale integration. Mott…
Vanadium dioxide (VO2) is a phase change material that can reversibly change between high and low resistivity states through electronic and structural phase transitions. Thus far, VO2 memory devices have essentially been volatile at room…
Quantum materials exhibiting phase transitions which can be controlled through external stimuli, such as electric fields, are promising for future computing technologies beyond conventional semiconductor transistors. Devices that take…
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We…
Implementation of neuromorphic hardware is a promising way to improve the computing efficiency and decrease the energy consumption of artificial neural networks. For this purpose, electronic elements emulating the behavior of synapses and…
Understanding and controlling phase transitions is a fundamental part of physics and has been central to many technological revolutions, from steam engines to field-effect transistors. At present, there is strong interest in materials with…
As artificial intelligence continues to grow, so does the need for more efficient ways to process data. Besides moving from electronic to photonic circuits, a promising approach is to integrate phase-change materials. Vanadium dioxide…
Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to…
The recent surge of interest in brain-inspired computing and power-efficient electronics has dramatically bolstered development of computation and communication using neuron-like spiking signals. Devices that can produce rapid and…
Vanadium dioxide (VO2) is one of the most promising materials for developing hybrid photonic integrated devices (PICs). However, despite switching times as low as a few femtoseconds have been reported, the all-optical temporal dynamics of…
Strongly correlated materials that exhibit an insulator-metal transition are key candidates in the search for new computing platforms. Understanding the pathways and timescales underlying the electrically-driven insulator-metal transition…
Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing. Neuromorphic computers aim to provide such a substrate that reproduces the brain's capabilities…
We demonstrate tuning of a metamaterial device that incorporates a form of spatial gradient control. Electrical tuning of the metamaterial is achieved through a vanadium dioxide layer which interacts with an array of split ring resonators.…
Conventional Artificial Intelligence (AI) systems are running into limitations in terms of training time and energy. Following the principles of the human brain, spiking neural networks trained with unsupervised learning offer a faster,…
Electrically driven insulator-metal transitions in prototypical quantum materials such as VO2 offer a foundational platform for designing novel solid-state devices. Tuning the V: O stoichiometry offers a vast electronic phase space with…
Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computational efficiency by mimicking the human brain. It is desired…
Memory effects during metal-insulator transitions in quantum materials reveal complex physics and potential for novel electronics mimicking biological neural systems. Nonetheless, understanding of memory and nonlinearity in sequential…