Related papers: Memristor nanodevice for unconventional computing:…
Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based…
Spintronics, a transformative field of research, leverages the spin of electron to revolutionize electronic devices, offering significant advantages over traditional charge-based systems. This chapter highlights the critical role of novel…
Memristor technologies have been rapidly maturing for the past decade to support the needs of emerging memory, artificial synapses, logic gates and bio-signal processing applications. So far, however, most concepts are developed by…
Nanomechanical resonator-based sensing devices are used in medical diagnostics based on their high-frequency dynamic behavior. Cantilevers fall into the category of Nanomechanical resonators. It also resembles a resonator whose shape is…
This study presents the design, fabrication, and test of a micro accelerometer with intrinsic processing capabilities, that integrates the functions of sensing and computing in the same MEMS. The device consists of an inertial mass…
Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der…
As data-intensive applications increasingly strain conventional computing systems, processing-in-memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by minimizing data transfer between memory and processing units.…
Neuromorphic computing promises to transform the current paradigm of traditional computing towards Non-Von Neumann dynamic energy-efficient problem solving. Thus, dynamic memory devices capable of simultaneously performing nonlinear…
A quantum memristor is a resistive passive circuit element with memory engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system-bath coupling is mediated…
The possible use of spin and magnets in place of charge and capacitors to store and process information is well known. Magnetic tunnel junctions are being widely investigated and developed for magnetic random access memories. These are two…
Nanomaterials, in addition to their small size, possess unique physicochemical properties that differ from the bulk materials, making them ideal for a host of novel applications. Magnetic nanoparticle (MNP) is one important class of…
The class of memory circuit elements which comprises memristive, memcapacitive, and meminductive systems, is gaining considerable attention in a broad range of disciplines. This is due to the enormous flexibility these elements provide in…
Memristive devices, whose resistance can be controlled by applying a voltage and further retained, are attractive as possible circuit elements for neuromorphic computing. This new type of devices poses a number of both technological and…
Development of future sensor, memory, and computing nanodevices based on novel physical concepts is one of the significant research endeavors in solid-state research. The field of spintronics is one such promising area of nanoelectronics…
Two-dimensional (2D) materials like transition metal dichalcogenides (TMD) have proved to be serious candidates to replace silicon in several technologies with enhanced performances. In this respect, the two remaining challenges are the…
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…
The review of studies on memristive properties or effect of resistive switchings in four classes of high temperature superconductors is presented in order to reveal functional properties of HTSCs which become apparent in the effects under…
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning is based on computational models that are, to a certain extent, bio-inspired, as…
The morphology and dimension of the conductive filament formed in a memristive device are strongly influenced by the thickness of its switching medium layer. Aggressive scaling of this active layer thickness is critical towards reducing the…
We show that ideal memristors - devices whose resistance is proportional to the charge that flows through them - can be realized using spin torque-driven viscous magnetization dynamics. The latter can be accomplished in the spin liquid…