The integration of nanoscale production processes with Artificial intelligence (AI) algorithms has the potential to open new frontiers in nanomanufacturing by accelerating development timelines, optimizing production, reducing costs, enhancing qualit...
In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO, structured as W/WO/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behav...
Memristors are an important component of the next-generation artificial neural network, high computing systems, etc. In the past, two-dimensional materials based memristors have achieved a high performance and low power consumption, though one at the...
On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully connected neural network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or...
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than tradit...
In this study, we investigate a proton-based three-terminal (3-T) synapse device to realize linear weight-update and I-V linearity characteristics for neuromorphic systems. The conductance states of the 3-T synapse device can be controlled by modulat...
Artificial synapses emulate biological synaptic signals in neuromorphic systems to attain brain-like computation and autonomous learning behaviors in non-von-Neumann systems. Several classes of materials have been applied to this field to achieve num...
In artificial neural networks, neurons are usually implemented with highly dissipative CMOS-based operational amplifiers. A more energy-efficient implementation is a 'spin-neuron' realized with a magneto-tunneling junction (MTJ) that is switched with...
To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC charact...
A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with ...