Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh +8 more
wiley +1 more source
Organic Electrochemical Transistors for Neuromorphic Devices and Applications. [PDF]
Xiang K, Song J, Liu H, Chen J, Yan F.
europepmc +1 more source
A New Approach to the Fabrication of Memristive Neuromorphic Devices: Compositionally Graded Films. [PDF]
Yoon JG.
europepmc +1 more source
Unconventional Hysteretic Charge Filling in Moiré‐Reconstructed Helical Trilayer Graphene
In helical trilayer graphene, sequential twisting reconstructs the moiré landscape into periodic domains separated by aperiodic boundaries. Longitudinal transport reveals sweep‐direction‐dependent hysteresis, while the Hall response traces this behavior to hysteretic charge filling at the aperiodic boundaries.
Hangyeol Park +9 more
wiley +1 more source
Polyoxometalates (POMs) Memristors/Neuromorphic Devices: From Structure Engineering to Material and Function Integration. [PDF]
Hu J, Xu S, Meng Y.
europepmc +1 more source
Electromagnetic Radiation Stimulated Learning in Perovskite Nickelates
ABSTRACT Biological plasticity refers to the ability of synapses to strengthen or weaken over time. These adaptive properties play a fundamental role in learning and memory, spanning many orders of magnitude in timescales. Short‐term plasticity (STP) arises from rapid correlative activity, while long‐term plasticity (LTP) is governed by slower ...
Ranjan Kumar Patel +8 more
wiley +1 more source
Single-molecule neuromorphic device
Abstract Artificial neural network-based machine learning provides foundations for artificial intelligence (AI), yet requires high energy costs for training. Beyond software-level simulation of neural networks, hardware-level implementation via neuromorphic devices becomes the next milestone in nanoscience towards energy-sustainable AI.
Wenjing Hong +20 more
openaire +1 more source
Sub-picojoule-per-bit volitional neuromorphic devices for precise targeting and tracking. [PDF]
Huang Y +16 more
europepmc +1 more source
Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications. [PDF]
Ko TJ +14 more
europepmc +1 more source
Reconfigurable Selector‐Only Memory (SOM) for Scalable Neuromorphic Computing
ABSTRACT Highly scalable reconfigurable neuromorphic devices are critical for addressing continual‐learning challenges in artificial intelligence. However, the scalability of existing reconfigurable devices is severely constrained by limited operating margins and insufficient process maturity.
Jin‐Yu Wen +7 more
wiley +1 more source

