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Neuromorphic computing is considered to have the potential to overcome the limitations of traditional von Neumann architecture due to its high efficiency, low energy consumption, and fault-tolerance. Hardware components that can emulate the synaptic plasticity of neurons, i.e. artificial synaptic devices, are required by neuromorphic systems. New devices have been examined for such components, such as phase-change artificial synapse, ferroelectric artificial synapse, and memristor synapses. Among them, memristor, a two-terminal metal-insulator-metal structure that are analogous to a biological synapse with presynaptic neuron (top electrode), postsynapticneuron (bottom electrode), and synaptic cleft (memristive film), is a promising device technology because of its tunable resistance, scalability, 3D integration compatibility, low power consumption, and relatively high speed. In contrary to inorganic materials such as metal oxides, natural organic materials have attracted interest to form the memristive layer because they are renewable, biodegradable, sustainable, biocompatible, and environmentally friendly. In this paper, honey solution embedded with carbon nanotubes (CNTs) was processed into the memristive layer by a low cost solution-based process, with synaptic plasticity of the final honey-CNT memristors characterized, including forget and relearn, spike-rate-dependent plasticity, spike-voltage-dependent plasticity, short-term to long-term memory transition, paired pulse facilitation, and spatial supra-linear summation behaviors. The successful emulation of these essential biological synaptic behaviors demonstrates the potential of honey-CNT memristors as a viable hardware component in neuromorphic computing systems.
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This content will become publicly available on July 15, 2026
Inkjet-Printed Memristor Device for Neuromorphic Computing: Fabrication, Modeling, and Reservoir Implementation
Co-localized memory and processing components are necessary for the creation of scalable neuromorphic hardware, and these components can be produced using inexpensive techniques. While memristors have emerged as promising candidates for synaptic emulation, most devices rely on complex fabrication processes that hinder large-scale deployment. This work presents a fully inkjet-printed memristor utilizing hexagonal boron nitride (hBN) and graphene inks, demonstrating a manufacturable approach to neuromorphic systems. The devices exhibit nonvolatile resistive switching with symmetric pinched hysteresis loops [Formula: see text] and tunable conductance states. Furthermore, a piece-wise nonlinear memristor model was developed and implemented in Simscape to enable system-level simulations. A [Formula: see text] reservoir computing architecture, constructed using these memristive devices, successfully demonstrated temporal pattern processing and feature extraction capabilities. The results establish printed memristors as viable building blocks for next-generation edge computing applications, combining the advantages of solution-processable materials with neuromorphic functionality.
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- Award ID(s):
- 2430440
- PAR ID:
- 10630892
- Publisher / Repository:
- World Scientific
- Date Published:
- Journal Name:
- International Journal of High Speed Electronics and Systems
- ISSN:
- 0129-1564
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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