相关论文: Antiferromagnetic multi-level memristor using line…
Ferromagnets are key materials for sensing and memory applications. In contrast, antiferromagnets that represent the more common form of magnetically ordered materials, have so far found less practical application beyond their use for…
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially…
Prospective spintronic memory and logic devices will benefit from the negligible stray field and ultrafast magnetic dynamics inherent to antiferromagnets [1]. However, realizing isothermal, nonvolatile, and deterministic switching of…
Despite all the progress of semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally…
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time.…
In conventional digital computers, data and information are represented in binary form and encoded in the steady states of transistors. They are then processed in a quasi-static way. However, with transistors approaching their physical…
Dielectric capacitors hold a tremendous advantage for energy storage due to their fast charge/discharge times and stability in comparison to batteries and supercapacitors. A key limitation to today's dielectric capacitors, however, is the…
Neuromorphic computing, inspired by biological intelligence, offers a pathway to revolutionize artificial intelligence (AI) by unifying memory and processing in an energy-efficient, sustainable framework for data-intensive tasks.…
Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning…
Spin-torque memristors were proposed in 2009, which could provide fast, low-power and infinite memristive behavior for large-density non-volatile memory and neuromorphic computing. However, the strict requirements of combining high…
Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the…
Solving complex tasks in a modern information-driven society requires novel materials and concepts for energy-efficient hardware. Antiferromagnets offer a promising platform for seeking such approaches due to their exceptional features: low…
The current work proposes a novel scheme for developing a light-activated non-filamentary memristor device by fabricating an Au-nanoparticle embedded HfO$_2$-bilayer/p-Si MOS structure. Under illumination, the electrons in such embedded…
In the present work we describe a way to assess memory capability of real devices, while proposing to the engineering community what to pursue to create devices with deep associated memory capability. The study of the signal produced by a…
The areal footprint of memristors is a key consideration in material-based neuromorophic computing and large-scale architecture integration. Electronic transport in the most widely investigated memristive devices is mediated by filaments,…
Controllable quantized conductance states of TiN/Ti/HfO$_x$/TiN memristors are realized with great precision through a pulse-mode reset procedure, assisted with analytical differentiation of the condition of the set procedure, which…
Spintronics has gone through substantial progress due to its applications in energy-efficient memory, logic and unconventional computing paradigms. Multilayer ferromagnetic thin films are extensively studied for understanding the domain…
A memristor is one of four fundamental two-terminal solid elements in electronics. In addition with the resistor, the capacitor and the inductor, this passive element relates the electric charges to current in solid state elements. Here we…
We demonstrate a non-volatile magnetoelectric magnonic memory (MEMM) that enables fully electrical write/read via direct magnon-driven sensing in an insulating antiferromagnet. A fabricated SrIrO3/La-BiFeO3/SrIrO3 trilayer exhibits sub-100…
Multiferroic materials, in which ferroelectric and magnetic ordering coexist, are of fundamental interest for the development of novel memory devices that allow for electrical writing and non-destructive magnetic readout operation. The…