Emerging Liquid‐Based Memristive Devices for Neuromorphic Computation

Small Methods, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Abstract To mimic the neural functions of human brain, developing hardware with natural similarities to nervous system is crucial for realizing neuromorphic computing architectures. Owing their capability emulate artificial neurons and synapses, memristors are widely regarded as a leading candidate achieving computing. However, most current memristor devices solid‐state. In contrast, biological systems operate within an aqueous environment, brain accomplishes intelligent behaviors such information generation, transmission, memory by regulating ion transport in neuronal cells. achieve that more analogous energy‐efficient, based on liquid environments developed. contrast traditional solid‐state memristors, liquid‐based possess advantages anti‐interference, low energy consumption, heat generation. Simultaneously, they demonstrate excellent biocompatibility, rendering them ideal option next generation intelligence systems. Numerous experimental demonstrations reported, showcasing unique memristive properties novel functionalities. This review focuses recent developments discussing operating mechanisms, structures, functional characteristics. Additionally, potential applications development directions proposed.

Language: Английский

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Nanofluidic Memristive Transition and Synaptic Emulation in Atomically Thin Pores

Nano Letters, Journal Year: 2025, Volume and Issue: unknown

Published: March 29, 2025

Ionic transport across nanochannels is the basis of communications in living organisms, enlightening neuromorphic nanofluidic iontronics. Comparing to angstrom-scale long biological ionic pathways, it remains a great challenge achieve memristors at such thinnest limit due ambiguous electrical model and interaction process. Here, we report atomically thin memristive nanopores two-dimensional materials by designing optimized conductance decouple memristive, ohmic, capacitive effects. By conducting different charged iontronics, realize reconfigurable transition between nonvolatile-bipolar volatile-unipolar characteristics, which arises from distinct processes governed energy barriers. Notably, emulate synaptic functions with ultralow consumption ∼0.546 pJ per spike reproduce learning behaviors. The are similar biosystems angstrom structure, rich iontronic responses, millisecond-level operating pulse width, matching potential width. This work provides new paradigm for boosting brain-inspired devices.

Language: Английский

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Resistance-Restorable Nanofluidic Memristor and Neuromorphic Chip

Nano Letters, Journal Year: 2025, Volume and Issue: unknown

Published: April 12, 2025

Resistance drift due to residual ions limits the accuracy of memristor-based neuromorphic computing. Here, we demonstrate nanofluidic memristors based on voltage-driven ion filling within Ångström channels, immersed in asymmetrically concentrated electrolyte solutions. Inspired by brain's waste clearance, restore conductance after 20,000 cycles removing trapped ions, paving way for endurance enhancement. The devices exhibit hour-long retention and ultralow energy consumption (∼0.2 fJ per spike channel). By tuning voltage, frequency, pH, emulate short-term synaptic plasticity. Finally, demonstrated first 4 × memristor array capable recognizing mathematical operators. Our work that fluidic are promising energy-efficient, long-retention, chips.

Language: Английский

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Recent advances in fluidic neuromorphic computing

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(2)

Published: April 21, 2025

Human brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor memory. At the core this unique capability are billions neurons connected through trillions synapses—basic units brain. The action potentials or “spikes” based temporal using regulated ions across ion channels in neuron cells allows sparse efficient transmission Emerging systems on confined fluidic have provided a framework for new type neuromorphic computing with lower energy consumption, hardware-level plasticity, multiple carriers that emulate natural processes mechanisms human These mimic neuronal architectures by harnessing modulating transport along artificial channels. spikes-induced ion-to-surface interactions within these enables control ionic conductivity to achieve synaptic plasticity realization brain-inspired functionalities such as memory effect signal transmission. Herein, review provides an overview recent advances devices memristors other components, covering their basic operations, materials architectures, well applications computing. concludes brief outline challenges emerging technologies face outlook development fluidic-based

Language: Английский

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Reconfigurable neuromorphic computing by a microdroplet

Cell Reports Physical Science, Journal Year: 2024, Volume and Issue: 5(9), P. 102202 - 102202

Published: Sept. 1, 2024

Language: Английский

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