Related papers: Nanocryotron-driven Charge Configuration Memristor
Memtranstor that correlates charge and magnetic flux via nonlinear magnetoelectric effects has a great potential in developing next-generation nonvolatile devices. In addition to multi-level nonvolatile memory, we demonstrate here that…
In recent years, neuromorphic computing has gained attention as a promising approach to enhance computing efficiency. Among existing approaches, neurotransistors have emerged as a particularly promising option as they accurately represent…
Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallization cell (ECM) nanodevices with tunable filamentary…
Understanding the movement of charge within organic semiconducting films is crucial for applications in photo-voltaics and flexible electronics. We study the sensitivity of the electrical conductance of a silicon nanowire to changes of…
Future development of the modern nanoelectronics and its flagships internet of things and artificial intelligence as well as many related applications is largely associated with memristive elements. This technology offers a broad spectrum…
The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such…
As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based…
We demonstrate that electroactive thin films incorporating semiconducting polymers and deterministic functionalized gold nanoclusters (ncAu25) lead to integration of the functions of resistive memory device and field-effect transistor…
The rapid development of machine learning and quantum computing has placed quantum machine learning at the forefront of research. However, existing quantum machine learning algorithms based on quantum variational algorithms face challenges…
Large-scale integration of emerging nanoscale non-volatile memory devices, e.g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems. Such…
Efficient and controlled charge transport in networks of semiconducting single-walled carbon nanotubes is the basis for their application in electronic devices, especially in field-effect transistors and thermoelectrics. The recent advances…
We use density functional theory calculations to study simple but diverse stoichiometries within the novel chemical capacitor (CC) setup. We look at main effects occurring in this device, extremes of the physicochemical properties, and we…
Designing lightweight convolutional neural network (CNN) models is an active research area in edge AI. Compute-in-memory (CIM) provides a new computing paradigm to alleviate time and energy consumption caused by data transfer in von Neumann…
In modern applications of heterogeneous liquid-phase nanocatalysis, the catalysts (e.g., metal nanoparticles) need to be typically affixed to a colloidal carrier system for stability and easy handling. "Passive carriers" (e.g., simple…
We have fabricated air-stable n-type, ambipolar carbon nanotube field effect transistors (CNFETs), and used them in nanoscale memory cells. N-type transistors are achieved by annealing of nanotubes in hydrogen gas and contacting them by…
The nonstoichiometric NaxCoO2 system exhibits extraordinary physical properties that correlate with temperature and Na concentration in its layered lattice without evident long-range structure modification when conventional crystallographic…
Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant…
We perform the characterization and modeling of a floating-gate device realized with a commercial 350-nm CMOS technology at cryogenic temperature. The programmability of the device offers a solution in the realization of a precise and…
Memristive switching in polycrystalline materials is widely attributed to the formation and rupture of conducting filaments, believed to be mediated by oxygen-vacancy redistribution. The underlying atomic-scale processes are still unknown,…
Understanding charging mechanisms and charge retention dynamics of nanocrystal memory devices is important in optimization of device design. Capacitance spectroscopy on PECVD grown germanium nanocrystals embedded in a silicon oxide matrix…