Memristive devices have garnered significant attention in the field of electronics over the past few decades. The reason behind this immense interest lies in the ubiquitous nature of memristive dynamics within nanoscale devices, offering the potential for revolutionary applications. These applications span from energy-efficient memories to the development of physical neural networks and neuromorphic computing platforms.
View Article and Find Full Text PDFThe brain's learning and adaptation processes heavily rely on the concept of associative memory. One of the most basic associative learning processes is classical conditioning. This work presents a memristive neural network-based associative memory system.
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May 2022
In designing ultra-efficient noise immune nanoscale circuits and systems, Schmitt triggers (STs) are vital components influencing total functionality. This article proposes an ultracompact ST using ferroelectric carbon nanotube field-effect transistors (Fe-CNTFETs) and a robust ST latch. By using the unique electrical futures of the Fe-CNTFETs, the proposed ST has been designed in a particular way to only employ two transistors similar to a conventional binary inverter.
View Article and Find Full Text PDFThe advanced neuro-computing field requires new memristor devices with great potential as synaptic emulators between pre- and postsynaptic neurons. This paper presents memristor devices with TiO Nanoparticles (NPs)/Ag(Silver) and Titanium Dioxide (TiO) Nanoparticles (NPs)/Au(Gold) electrodes for synaptic emulators in an advanced neurocomputing application. A comparative study between Ag(Silver)- and Au(Gold)-based memristor devices is presented where the Ag electrode provides the improved performance, as compared to the Au electrode.
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