Related papers: Interfacial and bulk switching MoS2 memristors for…
Enhancing the switching speed of oxide-based memristive devices at a low voltage level is crucial for their use as non-volatile memory and their integration into emerging computing paradigms such as neuromorphic computing. Efforts to…
This paper examines the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in memristive devices, focusing on ReRAM devices, specifically the interface-type or non-filamentary analog switching devices. A…
Previously, we demonstrated hysteretic and persistent changes of resistivity in two-terminal electronic devices based on charge trapping and detrapping at immobile metastable defects [H. Yin, A. Kumar, J.M. LeBeau, and R. Jaramillo, Phys.…
Reservoir computing is a subfield of machine learning in which a complex system, or 'reservoir,' uses complex internal dynamics to non-linearly project an input into a higher-dimensional space. A single trainable output layer then inspects…
High-density phase change memory (PCM) storage is proposed for materials with multiple intermediate resistance states, which have been observed in 1$T$-TaS$_2$ due to charge density wave (CDW) phase transitions. However, the metastability…
We have carried out a preliminary design and simulation of a single-electron resistive switch based on a system of two linear, parallel, electrostatically-coupled molecules: one implementing a single-electron transistor and another serving…
The necessity of having an electronic device working in relevant biological time scales with a small footprint boosted the research of a new class of emerging memories. Ag-based volatile resistive switching memories (RRAMs) feature a…
In 1971 the memristor was originally postulated as a new non-linear circuit element relating the time integrals of current and voltage. More recently researchers at HPLabs have linked the theoretical memristor concept to resistance…
Resistive switching devices are promising emerging non-volatile memories. However, one of the biggest challenges for resistive switching (RS) memory applications is the device-to-device (D2D) variability which is related to the intrinsic…
Using memristive properties common for the titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to 7-bit precision)…
We consider reservoirs in the form of liquid state machines, i.e., recurrently connected networks of spiking neurons with randomly chosen weights. So far only the weights of a linear readout were adapted for a specific task. We wondered…
The fundamental building blocks of modern silicon-based microelectronics, such as double gate transistors in non-volatile Flash memories, are based on the control of electrical resistance by electrostatic charging. Flash memories could soon…
We demonstrate reservoir computing with a physical system using a single autonomous Boolean logic element with time-delay feedback. The system generates a chaotic transient with a window of consistency lasting between 30 and 300 ns, which…
Quantum computing and neural networks show great promise for the future of information processing. In this paper we study a quantum reservoir computer (QRC), a framework harnessing quantum dynamics and designed for fast and efficient…
Biologically inspired recurrent neural networks, such as reservoir computers are of interest in designing spatio-temporal data processors from a hardware point of view due to the simple learning scheme and deep connections to Kalman…
Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction or/and prediction of complex physical systems. However, the…
Usually, resistive switching (RS) devices show digital RS memory (sharp SET and RESET process), which is most suitable for digital data storage applications. Some RS devices also manifest ideal memristive behavior or analog memory…
The prediction of complex dynamics remains an open problem across many domains of physics, where nonlinearities and multiscale interactions severely limit the reliability of conventional forecasting methods. Quantum reservoir computing…
Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems' ability to process information and learn locally on the device. Local…
We report, for CVD-grown monolayer MoS2, the very first results on temporal degradation of material and device performance under electrical stress. Both low and high field regimes of operation are explored at different temperatures, gate…