相关论文: Ultra-low Power Nanoelectromechanical Memory Based…
Free-standing, interconnected metallic nanowire networks with density as low as 40 mg/cm^{3} have been achieved over cm-scale areas, using electrodeposition into polycarbonate membranes that have been ion-tracked at multiple angles.…
In this work, we propose "TimeFloats," an efficient train-in-memory architecture that performs 8-bit floating-point scalar product operations in the time domain. While building on the compute-in-memory paradigm's integrated storage and…
Carbon nanotubes (CNTs) are currently considered a successor to silicon in future nanoelectronic devices. To realize this, controlled growth of defect-free nanotubes is required. Until now, the understanding of atomic-scale CNT growth…
CMOS-based microelectronics are limited to ~150{\deg}C and therefore not suitable for the extreme high temperatures in aerospace, energy, and space applications. While wide bandgap semiconductors can provide high-temperature logic,…
Carbon nanotubes are of central importance for applications in nano-electronics thanks to their exceptional transport properties. They can be used as sensors, for example in biological applications, provided that they are functionalized to…
Dynamical decoupling techniques are widely used to characterize and control the environments of solid-state quantum defects, enabling solid-state quantum memories and nanoscale quantum sensors. However, resolution is often limited by the…
High-density magnetic memories are key components in spintronics, quantum computing, and energy-efficient electronics. Reduced dimensionality and magnetic domain stability at the nanoscale are essential for the miniaturization of magnetic…
Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era causes a drastic increase in leakage power consumption and temperature-related…
A fresh look on carbon-based transistor channel materials like single-walled carbon nanotubes (CNT) and graphene nanoribbons (GNR) in future electronic applications is given. Although theoretical predictions initially promised that GNR…
Neuromorphic engineering concentrates the efforts of a large number of researchers due to its great potential as a field of research, in a search for the exploitation of the advantages of the biological nervous system and the brain as a…
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requirements and design…
The measured capacitance, modulus and strength of carbon nanotube-polyaniline (CNT-PANI) composite electrodes render them promising candidates for structural energy storage devices. Here, CNT-PANI composite electrodes are manufactured with…
Magnetic random access memory (MRAM) is a leading emergent memory technology that is poised to replace current non-volatile memory technologies such as eFlash. However, the scaling of MRAM technologies is heavily affected by…
Microwave quantum memory promises advanced capabilities for noisy intermediate-scale superconducting quantum computers. Existing approaches to microwave quantum memory lack complete combination of high efficiency, long storage time,…
Phase-change memory (PCM) is a scalable and low latency non-volatile memory (NVM) technology that has been proposed to serve as storage class memory (SCM), providing low access latency similar to DRAM and often approaching or exceeding the…
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing…
The rapid advancement of neuromorphic technology aims to address the memory wall challenge inherent in conventional von Neumann architectures. This paper critically examines current digital neuromorphic processors and their strategies to…
With the development of novel computing schemes working at cryogenic temperatures, superconducting memory elements have become essential. In this context, superconducting quantum interference devices (SQUIDs) are promising candidates, as…
Neuro-inspired computing architectures are one of the leading candidates to solve complex, large-scale associative learning problems. The two key building blocks for neuromorphic computing are the synapse and the neuron, which form the…
With the emergence of IoT (Internet of things), huge amounts of sensitive data are being processed and transmitted everyday in edge devices with little to no security. Due to their aggressive power management schemes, it is a common and…