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Novel non-volatile memory devices are under intense investigation to revolutionize information processing for ultra-energy-efficient implementation of artificial intelligence and machine learning tasks. Ferroelectric memory devices with ultra-low power and fast operation, non-volatile data retention and reliable switching to multiple polarization states promise one such option for memory and synaptic weight elements in neuromorphic hardware. For quick adaptation by industry, complementary metal oxide semiconductor process compatibility is a key criterion that led to huge attention to hafnia-based FE materials. Designing a high endurance hafnia-based FE is crucially important for online training applications in neuromorphic hardware. In this work, we report on the physical origins of fatigue and recovery mechanisms in back-end-of-line compatible ferroelectric HfZrO thin film capacitors for designing high-endurance memory devices. We show that HfZrO devices are capable of recovery from the fatigue state with less than 5 V pulse sweeps. Such recovery has been conducted multiple times reaching 88%-93% of 2 upon each retrieval. This result indicates that with specifically engineered material stacking and annealing protocols, it is possible to reach endurance exceeding 10 cycles at room temperature, leading to ultralow power ferroelectric non-volatile memory components or synaptic weight elements compatible with online training tasks for neuromorphic computing.
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http://dx.doi.org/10.1039/d4nr04861j | DOI Listing |
Nat Commun
September 2025
Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Biological nervous systems constitute important sources of inspiration towards computers that are faster, cheaper, and more energy efficient. Neuromorphic disciplines view the brain as a coevolved system, simultaneously optimizing the hardware and the algorithms running on it. There are clear efficiency gains when bringing the computations into a physical substrate, but we presently lack theories to guide efficient implementations.
View Article and Find Full Text PDFAdv Mater
September 2025
Center for High Pressure Science (CHiPS), State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao, 066004, China.
Neuromorphic computing presents a promising solution for the von Neumann bottleneck, enabling energy-efficient and intelligent sensing platforms. Although 2D materials are ideal for bioinspired neuromorphic devices, achieving multifunctional synaptic operations with simple configurations and linear weight updates remains challenging. Inspired by biological axons, the in-plane anisotropy of 2D NbGeTe is exploited to develop dual electronic-optical synaptic devices.
View Article and Find Full Text PDFNanomaterials (Basel)
August 2025
National Institute of Materials Physics, Atomistilor 405A, 077125 Magurele, Romania.
Integrating two-dimensional transition-metal dichalcogenides with graphene is attractive for low-power memory and neuromorphic hardware, yet sequential wet transfer leaves polymer residues and high contact resistance. We demonstrate a complementary metal-oxide-semiconductor (CMOS)-compatible, transfer-free route in which an atomically thin amorphous MoS precursor is RF-sputtered directly onto chemical vapor-deposited few-layer graphene and crystallized by confined-space sulfurization at 800 °C. Grazing-incidence X-ray reflectivity, Raman spectroscopy, and X-ray photoelectron spectroscopy confirm the formation of residue-free, three-to-four-layer 2H-MoS (roughness: 0.
View Article and Find Full Text PDFBiomimetics (Basel)
August 2025
Institut de Microelectrònica de Barcelona, IMB-CNM (CSIC), Bellaterra, 08193 Barcelona, Spain.
Traditional brain emulation approaches often rely on classical computational models that inadequately capture the stochastic, nonlinear, and potentially coherent features of biological neural systems. In this position paper, we introduce NeuroQ a quantum-inspired framework grounded in stochastic mechanics, particularly Nelson's formulation. By reformulating the FitzHugh-Nagumo neuron model with structured noise, we derive a Schrödinger-like equation that encodes membrane dynamics in a quantum-like formalism.
View Article and Find Full Text PDFAdv Mater
August 2025
State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, 100029, China.
The selective attention mechanisms inherent in the human visual system provide a promising framework for developing edge systems that can simultaneously prune and process critical information from visual input. However, conventional complementary metal-oxide-semiconductor-based edge vision systems rely on complex digital logic for data pruning, alongside the physical separation of pruning, memory, and processing. This increases both power consumption and latency.
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