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Engineered nanoparticle network models for autonomous computing
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DOI：10.1063/5.0048898.png)
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Abstract
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Materials that exhibit synaptic properties are a key target for our effort to develop computing devices that mimic the brain intrinsically. If successful, they could lead to high performance, low energy consumption, and huge data storage. A 2D square array of engineered nanoparticles (ENPs) interconnected by an emergent polymer network is a possible candidate. Its behavior has been observed and characterized using coarse-grained molecular dynamics (CGMD) simulations and analytical lattice network models. Both models are consistent in predicting network links at varying temperatures, free volumes, and E-field (E) strengths. Hysteretic behavior, synaptic short-term plasticity and long-term plasticity-necessary for brain-like data storage and computing-have been observed in CGMD simulations of the ENP networks in response to E-fields. Non-volatility properties of the ENP networks were also confirmed to be robust to perturbations in the dielectric constant, temperature, and affine geometry. Published under license by AIP Publishing.
Keywords:
SELF-ASSEMBLED MONOLAYERS
GOLD NANOPARTICLES
MOLECULAR-DYNAMICS
MEMRISTORS
DEVICES
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