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Homeostasis is essential in biological neural networks, optimizing information processing and experience-dependent learning by maintaining the balance of neuronal activity. However, conventional two-terminal memristors have limitations in implementing homeostatic functions due to the absence of global regulation ability. Here, three-terminal oxide memtransistor-based homeostatic synapses are demonstrated to perform highly linear synaptic weight update and enhanced accuracy in neuromorphic computing. Particularly, by leveraging the gate control of contact-engineered indium-gallium-zinc-oxide (IGZO) memtransistor, synaptic weight scaling is enabled for high-linearity and precision neuromorphic computing. Moreover, sinusoidal control of gate voltage is demonstrated, possibly enabling the emulation of higher-order synaptic functions. The device structure of IGZO memtransistor is optimized regarding the source/drain electrode materials and an interfacial layer inserted between the IGZO channel and source electrode. As a result, memtransistors exhibiting high current switching ratio of >10 and reliable endurance characteristics are obtained. Furthermore, through the adaptation of synaptic scaling, emulating the homeostasis, non-linearity values of 0.01 and -0.01 are achieved for potentiation and depression, respectively, exhibiting a recognition accuracy of 91.77% for digit images. It is envisioned that the contact-engineered IGZO memtransistors hold significant promise for implementing the homeostasis in neuromorphic computing for high linearity and high efficiency.
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http://dx.doi.org/10.1002/smll.202409510 | DOI Listing |
Mater 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:
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.
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