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We report on an artificial synapse, an organic synapse-transistor (synapstor) working at 1 volt and with a typical response time in the range 100-200 ms. This device (also called NOMFET, Nanoparticle Organic Memory Field Effect Transistor)…
Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant…
Nanofluidics, the field interested in flows at the smallest scales, has grown at a fast pace, reaching an ever finer control offluidic and ionic transport at the molecular level. Still, artificial pores are far from reaching the wealth of…
As global data generation continues to rise, there is an increasing demand for revolutionary in-memory computing methodologies and efficient machine learning solutions. Despite recent progress in electrical and electro-optical simulations…
Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and…
We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and…
With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in…
Fluorite ferroelectrics are exciting candidates for next-generation non-volatile memory devices because their unique ferroelectric mechanism, which arises from unconventional oxygen displacements, permits ferroelectricity with minimal…
Since the first report of ferroelectricity in nanoscale HfO$_2$-based thin films in 2011, this silicon-compatible binary oxide has quickly garnered intense interest in academia and industry, and continues to do so. Despite its deceivingly…
Event-based neuromorphic systems provide a low-power solution by using artificial neurons and synapses to process data asynchronously in the form of spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memory devices and are…
Emulating the neural-like information processing dynamics of the brain provides a time and energy efficient approach for solving complex problems. While the majority of neuromorphic hardware currently developed rely on large arrays of…
In this letter, we quantify the impact of device limitations on the classification accuracy of an artificial neural network, where the synaptic weights are implemented in a Ferroelectric FET (FeFET) based in-memory processing architecture.…
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…
Interconnectivity, fault tolerance, and dynamic evolution of the circuitry are long sought-after objectives of bio-inspired engineering. Here, we propose dendritic transistors composed of organic semiconductors as building blocks for…
Atomic-scale understanding of HfO2 ferroelectricity is important to help address many challenges in developing reliable and high-performance ferroelectric HfO2 (fe-HfO2) based devices. Though investigated from different angles, a factor…
Ferroelectrics offer a promising materials platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET…
Among various types of neuromorphic devices towards artificial intelligence, the electrochemical synaptic transistor emerges, in which the channel conductance is modulated by the insertion of ions according to the history of gate voltage…
Ferroelectrics feature spontaneous electric dipolar order reconfigurable via electric fields. Recent theoretical studies of the collective excitations of this electric dipolar order give rise to the hope that "ferron" quasiparticles may…
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems. Though many devices have emerged as candidates for…
The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing. Hardware implementations of a neuromorphic system aims to mimic the computations in…