Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
[HTML][HTML] Reliability of analog resistive switching memory for neuromorphic computing
As artificial intelligence calls for novel energy-efficient hardware, neuromorphic computing
systems based on analog resistive switching memory (RSM) devices have drawn great …
systems based on analog resistive switching memory (RSM) devices have drawn great …
Stimuli‐responsive memristive materials for artificial synapses and neuromorphic computing
Neuromorphic computing holds promise for building next‐generation intelligent systems in a
more energy‐efficient way than the conventional von Neumann computing architecture …
more energy‐efficient way than the conventional von Neumann computing architecture …
A self-rectifying synaptic memristor array with ultrahigh weight potentiation linearity for a self-organizing-map neural network
H Zhang, B Jiang, C Cheng, B Huang, H Zhang… - Nano Letters, 2023 - ACS Publications
Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-
density and efficient neuromorphic computing, especially for future three-dimensional …
density and efficient neuromorphic computing, especially for future three-dimensional …
Oxide-based resistive switching-based devices: fabrication, influence parameters and applications
In advanced computing technologies, metal oxide-based resistive switching random access
memory (RRAM) has been considered an excellent scientific research interest in the areas …
memory (RRAM) has been considered an excellent scientific research interest in the areas …
Discrete memristor and discrete memristive systems
In this paper, we investigate the mathematical models of discrete memristors based on
Caputo fractional difference and G–L fractional difference. Specifically, the integer-order …
Caputo fractional difference and G–L fractional difference. Specifically, the integer-order …
Analog Synaptic Transistor with Al-Doped HfO2 Ferroelectric Thin Film
Neuromorphic computing has garnered significant attention because it can overcome the
limitations of the current von-Neumann computing system. Analog synaptic devices are …
limitations of the current von-Neumann computing system. Analog synaptic devices are …
HfO2-based resistive switching memory devices for neuromorphic computing
HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …
such as high scalability, fast switching speed, low power, compatibility with complementary …
Spiking neural network (snn) with memristor synapses having non-linear weight update
Among many artificial neural networks, the research on Spike Neural Network (SNN), which
mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor …
mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor …
Oxygen Vacancy Transition in HfOx‐Based Flexible, Robust, and Synaptic Bi‐Layer Memristor for Neuromorphic and Wearable Applications
In this work, a reliable bilayer flexible memristor is demonstrated using TaOx/HfOx Bi‐layer
(BL) to mimic synaptic characteristics by using oxygen concentration engineering in the …
(BL) to mimic synaptic characteristics by using oxygen concentration engineering in the …