相关论文: Carbon Based Resistive Memory
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…
Carbon foams are hypothetical carbon allotropes that contain graphite-like (sp$^2$ carbon) segments, connected by sp$^3$ carbon atoms, resulting in porous structures. In this work the DFTB (Density Functional based Tight-Binding) method…
Functional oxides based resistive memories are recognized as potential candidate for the next-generation high density data storage and neuromorphic applications. Fundamental understanding of the compositional changes in the functional…
Nanomaterials have much improved properties compared to their bulk counterparts, which promotes them as ideal material for applications in various industries. Among the various nanomaterials, different nanoallotropes of carbon, namely…
The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with conventional ECC, and has to deal with device unreliability.…
Resistive random-access memory (RRAM) is gaining popularity due to its ability to offer computing within the memory and its non-volatile nature. The unique properties of RRAM, such as binary switching, multi-state switching, and device…
We report on the transport properties of novel carbon nanostructures made of partially unzipped carbon nanotubes, which can be regarded as a seamless junction of a tube and a nanoribbon. We find that graphene nanoribbons act at certain…
Transition-metal oxide films, demonstrating the effects of both threshold and nonvolatile memory resistive switching, have been recently proposed as candidate materials for storage-class memory. In this work we describe some experimental…
Memory has always been a building block element for information technology. Emerging technologies such as artificial intelligence, big data, the internet of things, etc., require a novel kind of memory technology that can be energy…
As computing power demands continue to grow, superconducting electronics present an opportunity to reduce power consumption by increasing the energy efficiency of digital logic and memory. A key milestone for scaling this technology is the…
Memory cells are an important building block of digital electronics. We combine here the unique electronic properties of semiconducting monolayer MoS2 with the high conductivity of graphene to build a 2D heterostructure capable of…
Charge-trap memory with high-\k dielectric materials is considered to be a promising candidate for next-generation memory devices. Ultrathin layered two-dimensional (2D) materials like graphene and MoS2 have been receiving much attention…
Carbon nanotubes with their outstanding electrical and mechanical properties are suggested as interconnect material of the future and as switching devices, which could outperform silicon devices. In this paper we will introduce nanotubes,…
Cryo-computing - both classical and quantum, is severely limited by the absence of a suitable cryo-memory. The challenge both in terms of energy efficiency and speed have been known for decades, but so far conventional technologies have not…
A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based…
Current portable memory device relies heavily on flash memory technology for its implementation. New generation of non-volatile memory is likely to replace floating gates, charge-trapping memory currently still suffering from inadequate…
Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and…
The human brain, with its energy-efficient and massively parallel architecture seamlessly integrates memory and computation. Its topology and functionality serve as the inspiration for the field of neuromorphic computing. Realizing…
Resistive random access memory (RRAM) is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to…
A new concept for nonvolatile superconducting memories is proposed. The devices combine ferromagnetic dots for the storage of the data and Josephson junctions for their readout. Good scalability is expected for large scale integration.…