# Nanoparticle Networks for Neuromorphic Computing

arXiv: https://arxiv.org/abs/2607.27844 · HTML: https://mlsanity.com/paper/2607.27844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [Optimizing Memory in Reservoir Computers](https://mlsanity.com/paper/2201.01605/) (2022)
- [Integrated Photonic Reservoir Computing with All-Optical Readout](https://mlsanity.com/paper/2306.15845/) (2023)
- [A kinetic Monte Carlo Approach for Boolean Logic Functionality in Gold Nanoparticle Networks](https://mlsanity.com/paper/2312.04717/) (2023)
- [Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks](https://mlsanity.com/paper/2312.16037/) (2023)

## Abstract (arXiv)

Physical computing leverages complex dynamical systems for energy-efficient data processing. In this work, we present a neuromorphic architecture based on metallic nanoparticles interconnected by molecular junctions on a SiO2/Si substrate. We demonstrate that surrounding static control electrodes transform this nanoparticle network from a passive reservoir into a tunable nonlinear dynamical system. By analyzing how these electrodes route simple one-dimensional voltage inputs into multidimensional signal responses, we establish three core design rules to maximize computational performance. First, operating near the system's cutoff frequency achieves an optimal balance between nonlinear charge tunneling and linear capacitive memory. Second, tuning the underlying SiO2 thickness sets the electrostatic screening length and dictates the memory type. Thick oxide layers reduce the screening length, causing networks larger than this length to transition into a persistent, non-volatile-like regime. Conversely, networks smaller than the screening length exhibit only fading memory. Third, introducing structural disorder via heterogeneous molecular junctions overcomes inherent limits on expressivity. While a network's computational expressivity scales with its physical size, it is ultimately capped by the screening length. Breaking internal spatial symmetries with localized disorder bypasses this saturation, allowing control voltages to independently manipulate specific signal amplitudes and phases, universally maximizing performance for dynamic neuromorphic applications.