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Memristors with controllable resistive switching (RS) behavior have been considered as promising candidates for synaptic devices in next-generation neuromorphic computing. In this work, two-terminal memristors with controllable digital and analog RS behavior are fabricated based on two-dimensional (2D) WSe nanosheets. Under a relatively high operating voltage of 4 V, the memristor demonstrates stable and reliable non-volatile bipolar digital RS with a high switching ratio of 6.3 × 10. On the other hand, under a relatively low operation voltage, the memristor exhibits analog RS with a series of tunable resistance states. The fabricated memristors can work as an artificial synapse with fundamental synaptic functions, such as long-term potentiation (LTP) and depression (LTD) as well as paired-pulse facilitation (PPF). More importantly, the memristor demonstrates high conductance modulation linearity with the calculated nonlinear parameter for conductance as -0.82 in the LTP process, which is beneficial to improving the accuracy of neuromorphic computing. Furthermore, the neuromorphic computing of file types and image recognition can be emulated based on a constructed three-layer artificial neural network (ANN) with a recognition accuracy that can reach up to 95.9% for small digits. In addition, memristors can be used to emulate the learning-forgetting experience of the human brain. Consequently, the memristor based on 2D WSe nanosheets not only exhibits controllable RS behavior but also simulates synaptic functions and is expected to be a potential candidate for future neuromorphic computing applications.
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http://dx.doi.org/10.1039/d2nr06580k | DOI Listing |
J Phys Chem Lett
September 2025
Tianjin Key Laboratory of Film Electronic and Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
Achieving UVA/B-selective, skin-inspired nociceptors with perception and blockade functions at the single-unit device level remains challenging. This is because the device necessitates distinct components for every performance metric, thereby leading to complex preparation processes and restricted performance, as well as the absence of deep UV (UVB and below)-selective semiconductors. Here, to address this, we develop a structure-simplification skin-inspired nociceptor using a reverse type-II CuAgSbI/MoS heterostructure.
View Article and Find Full Text PDFMater Horiz
September 2025
Faculty of Science, School of Chemistry and Physics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia.
Organic electrochemical transistors (OECTs) continue to be the subject of much detailed and systematic study, being suitable for a diverse range of applications including bioelectronics, sensors, and neuromorphic computing. OECTs conventionally use a liquid electrolyte, and this architecture is well suited for sensing or bio-interfacing applications where biofluids or liquid samples can be used directly as the electrolyte. A more recent trend is solid-state OECTs, where a solid or semi-solid electrolyte such as an ion gel, hydrogel or polyelectrolyte replaces the liquid component for an all-solid-state device.
View Article and Find Full Text PDFJ Colloid Interface Sci
September 2025
School of Electronic Information & Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.
The integration of information memory and computing enabled by nonvolatile memristive device has been widely acknowledged as a critical solution to circumvent the von Neumann architecture limitations. Herein, the Au/NiO/CaBiTiO/FTO (CBTi/NiO) heterojunction based memristor with varying film thicknesses are demonstrated on FTO/glass substrates, and the CBTi/NiO-4 sample shows the optimal memristor characteristics with 5 × 10 stable switching cycles and 10-s resistance state retention. The electrical conduction in the low-resistance state is dominated by Ohmic behavior, while the high-resistance state exhibited characteristics consistent with the space-charge-limited conduction (SCLC) model.
View Article and Find Full Text PDFNeural Netw
August 2025
The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, 8140, New Zealand. Electronic address:
The biological brain is comprised of a complex, interconnected, self-assembled network of neurons and synapses. This network enables efficient and accurate information processing, unsurpassed by any other known computational system. Percolating networks of nanoparticles (PNNs) are complex, interconnected, self-assembled systems that exhibit many emergent brain-like characteristics.
View Article and Find Full Text PDFSci Bull (Beijing)
August 2025
School of Physics and Technology, Wuhan University, Wuhan 430072, China. Electronic address: