Branch model, random-memristor network, and Hopfield model in Electrical memorability in silver nanoparticle composite operated at the conductor-to-insulator (CtI) percolation threshold: Why should it be operated at CtI threshold?
Aeneas-Atlas Wei-Chi Hsu1*, Hua-Yan Chen2, Tsun Hsu Chang1,2
1College of Semiconductor, National Tsing-Hua University, Hsinchu, Taiwan
2Department of Physics, National Tsing-Hua University, Hsinchu, Taiwan
* Presenter:Aeneas-Atlas Wei-Chi Hsu, email:weichihsu1996@gmail.com
The static properties of metal-nanoparticle composites have been meticulously explored over recent years, often analyzed through percolation theory or statistical mechanics. In our research, however, we demonstrate that Silver-nanoparticle composites, when operated at the conductor-to-insulator (CtI) threshold, exhibit a capacity for what could be termed “memorability.” This outcome diverges from conventional percolation theory predictions, becoming evident only when concepts from neuroscience were integrated into our approach. In particular, a tunable sigma ratio in our branching model shows parallels to the Hopfield model, providing a potential interpretive framework for our findings. Notably, we opted to use “memorability” rather than terms like “memory” or “memristor” to clarify conceptual distinctions. This brief report aims to alleviate cross-disciplinary misunderstandings while highlighting the application of Silver-nanoparticle composites in information storage and electrical circuits, underscoring the need for further optimization of material properties and operational parameters to enhance performance.
Keywords: Memorability, Branch Model, Conductor-to-insulator threshold, Information storage applications, Metal Nanoparticle composite