Related papers: Gate-controlled neuromorphic functional transition…
We propose a quantum gate architecture that allows for the systematic control of the effective exchange interactions between magnetic impurities embedded in nano-scale graphene flakes connected by a gated bridge. The entanglement between…
Flexible metal oxide/graphene oxide hybrid multi-gate neuron transistors were fabricated on flexible graphene substrates. Dendritic integrations in both spatial and temporal modes were successfully emulated, and spatiotemporal correlated…
In this letter, we demonstrate a non-volatile memory device in a graphene FET structure using ferroelectric gating. The binary information, i.e. "1" and "0", is represented by the high and low resistance states of the graphene working…
Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to…
Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer far less computational complexity than their biological counterparts. Neurons have dendritic arbors that connect to…
The effects of thermal fluctuations on the morphology of two-dimensional materials are hard to harness. We propose that a geometrically constrained graphene nanoribbon (GNR) can exhibit thermally activated snap-through transitions with a…
Despite all the progress of semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally…
Optical wave-based computing has enabled the realization of real-time information processing in both space and time domains. In the past few years, analog computing has experienced rapid development but mostly for a single function.…
An energy-efficient voltage controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling in the presence of room temperature thermal noise. By controlling the domain wall motion…
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking…
The absence of a band gap in graphene restricts its straight forward application as a channel material in field effect transistors. In this letter, we report on a new approach to engineer a band gap in graphene field effect devices (FED) by…
A nanoscale device consisting of a metal nanowire, a dielectric, and a gate is proposed. A combination of quantum and thermal stochastic effects enable the device to have multiple functionalities, serving alternately as a transistor, a…
Aside from recent advances in artificial intelligence (AI) models, specialized AI hardware is crucial to address large volumes of unstructured and dynamic data. Hardware-based AI, built on conventional complementary metal-oxidesemiconductor…
The excellent mechanical properties make graphene promising for realizing nanomechanical resonators with high resonant frequencies, large quality factors, strong nonlinearities, and the capability to effectively interface with various…
Artificial neural networks and computational neuroscience models have made tremendous progress, allowing computers to achieve impressive results in artificial intelligence (AI) applications, such as image recognition, natural language…
Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant…
It is shown that one can explore the optical conductivity of graphene, together with the ability of controlling its electronic density by an applied gate voltage, in order to achieve resonant coupling between an external electromagnetic…
We report a detailed study of neuromorphic switching behaviour in inherently complex percolating networks of self-assembled metal nanoparticles. We show that variation of the strength and duration of the electric field applied to this…
Neuron is the most important building block in our brain, and information processing in individual neuron involves the transformation of input synaptic spike trains into an appropriate output spike train. Hardware implementation of neuron…
Graphene electrodes provide a suitable alternative to metal contacts in molecular conduction nanojunctions. Here, we propose to use graphene electrodes as a platform for effective photon assisted tunneling through molecular conduction…