相关论文: Projected mushroom-type phase-change memory
Ferroelectric materials with switchable electric polarization hold great promise for a plethora of emergent applications, such as post-Moore's law nanoelectronics, beyond-Boltzmann transistors, non-volatile memories, and above-bandgap…
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as…
As modern computing gets continuously pushed up against the von Neumann Bottleneck -- limiting the ultimate speeds for data transfer and computation -- new computing methods are needed in order to bypass this issue and keep our computer's…
A reservoir computer is a way of using a high dimensional dynamical system for computation. One way to construct a reservoir computer is by connecting a set of nonlinear nodes into a network. Because the network creates feedback between…
In the last decade, a 2-terminal passive circuit element called a memristor has been developed for non-volatile resistive random access memory and has more recently shown promise for neuromorphic computing. Compared to flash memory,…
Non-volatile resistive switching, also known as memristor effect in two terminal devices, has emerged as one of the most important components in the ongoing development of high-density information storage, brain-inspired computing, and…
Controlling phase transitions in quantum systems via coupling to reservoirs has been mostly studied for idealized memory-less environments under the so-called Markov approximation. Yet, most quantum materials and experiments in the solid…
The memristive device is one of the basic elements of novel, brain-inspired, fast, and energy-efficient information processing systems in which there is no separation between memorization and information analysis functions. Since the first…
The thermal properties of amorphous and crystalline phases in chalcogenide phase change materials (PCM) play a key role in device performance for non-volatile random-access memory. Here, we report the nanothermal morphology of amorphous and…
We show theoretically that networks of membrane memcapacitive systems -- capacitors with memory made out of membrane materials -- can be used to perform a complete set of logic gates in a massively parallel way by simply changing the…
Photonic computing using chalcogenide phase-change materials (PCMs) is under active development for energy-efficient artificial intelligence (AI) applications. A key requirement is to enable as many optically programmable levels per device…
Resistance Random Access Memory (RRAMTM) device, with its electrically induced nanoscale resistive switching capacity, has been gaining considerable attention as future non-volatile memory device. Here, we propose a mechanism of switching…
Non-volatile magnetic storage, from 1940s magnetic core to present day racetrack memory and magnetic anisotropy switching devices rely on the metastability of magnetic domains to store information. However, the inherent inefficiency of…
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory…
We present a computationally inexpensive yet accurate phenomenological model of memristive behavior in titanium dioxide devices by fitting experimental data. By design, the model predicts most accurately I-V relation at small non-disturbing…
If a computer could be assembled from superconducting components, the energy efficiency would far surpass that of conventional electronics. Historic research efforts towards this goal yielded pivotal breakthroughs in the development and…
The growing energy demands of information and communication technologies, driven by data-intensive computing and the von Neumann bottleneck, underscore the need for energy-efficient alternatives. Resistive random-access memory (RRAM)…
With the help of quantum mechanics one can formulate a model of associative memory with optimal storage capacity. I generalize this model by introducing a parameter playing the role of an effective temperature. The corresponding…
Digital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding ordinary differential equations depend on a few…
Phase-change materials based on Ge-Sb-Te alloys are widely used in industrial applications such as nonvolatile memories, but reaction pathways for crystalline-to-amorphous phase-change on picosecond timescales remain unknown. Femtosecond…