Related papers: Nanofluidic logic with mechano-ionic memristive sw…
The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find…
With the development of novel computing schemes working at cryogenic temperatures, superconducting memory elements have become essential. In this context, superconducting quantum interference devices (SQUIDs) are promising candidates, as…
Since the experimental discovery of magnetic skyrmions achieved one decade ago, there have been significant efforts to bring the virtual particles into all-electrical fully functional devices, inspired by their fascinating physical and…
Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be…
Developing ultra-low-energy superconducting computing and fault-tolerant quantum computing will require scalable superconducting memory. While conventional superconducting logic-based memory cells have facilitated early demonstrations,…
Memristors offer significant advantages as in-memory computing devices due to their non-volatility, low power consumption, and history-dependent conductivity. These attributes are particularly valuable in the realm of neuromorphic circuits…
Compact models of memristors are essential for simulating large-scale neuromorphic systems, yet they often do not include description of complex dynamics like volatile relaxation and synaptic plasticity. We introduce a modular,…
We present an integrated iontronic memristor circuit that reproduces biologically inspired Spike Rate-Dependent Plasticity (SRDP) and functions as a physical nonlinear frequency kernel, which we demonstrate can be used to classify natural…
All-optical active plasmonic devices are of fundamental importance for designing efficient nanophotonic circuits. We theoretically propose and numerically investigate an active plasmonic device made up of a nonlinear epsilon-near-zero…
The quest for energy-efficient, scalable neuromorphic computing has elevated compute-in-memory (CIM) architectures to the forefront of hardware innovation. While memristive memories have been extensively explored for synaptic implementation…
The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has prevented widespread adoption. Here we show coherent electron wave…
Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and…
Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic…
Our study demonstrates that strong cationic segregation can occur in amorphous complex oxide memristors during electrical operation. With the help of analytic techniques, we observed that switching the electrical stimulation from voltage to…
Redox-based nanoionic resistive memory cells (ReRAMs) are one of the most promising emerging nano-devices for future information technology with applications for memory, logic and neuromorphic computing. Recently, the serendipitous…
A superconducting loop stores persistent current without any ohmic loss, making it an ideal platform for energy efficient memories. Conventional superconducting memories use an architecture based on Josephson junctions (JJs) and have…
We present an integrated switch that combines plasmonic and neuromorphic technologies with a single sub-stoichiometric VO2-x nanoparticle. The presented device acts as a versatile plasmonic switch with dual thermal and electrical…
A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS association. Among various…
This article presents the implementation of on-chip iontronic circuits via small-scale integration of multiple ionic logic gates made of bi-polar polyelectrolyte diodes. These ionic circuits are analogous to solid-state electronic circuits,…
Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to…