journal article·2025-09-23

Reservoir Computing Using Series-Parallel-Connected Au Nanogaps and Electromigrated Coulomb Islands

Keita Sakai, Yudai Tanaka, Mamiko Yagi, Mitsuki Ito, Jun-ichi Shirakashi

ACS Applied Electronic Materials· Volume 7· Issue 18
Publisher page

Abstract

Reservoir computing necessitates the development of devices exhibiting complex dynamic characteristics to enable efficient hardware implementation. This study introduces a physical reservoir computing (PRC) scheme based on series-parallel-connected Au nanogaps activated through a specialized technique. The method utilizes electromigration, driven by field emission currents across the nanogaps, to modulate the tunnel resistance. The memory capacities of physical reservoirs configured as 2 × 1 and 3 × 2 series-parallel-connected nanogaps were experimentally evaluated by using short-term memory (STM) and parity check (PC) tasks. Unlike single-nanogap systems, these series-parallel configurations do not exhibit a reservoir property region characterized by high STM capacity and low PC capacity. Instead, they demonstrate enhanced memory performance in both STM and PC tasks. Compared with systems based on single Au nanogaps, these configurations significantly improve STM and PC capabilities without increasing the number of virtual nodes, thereby preserving the processing speed inherent to single-nanogap systems. Furthermore, scanning electron microscopy revealed structural modifications within the nanogaps after reservoir operation, including the formation of single-electron transistor islands, which may enhance computational capabilities through single-electron tunneling effects. Evidence of Coulomb blockade behavior, observed as a distinct suppression of conductance near zero bias voltage in the drain current–drain voltage characteristics, further supports this enhancement. These results establish series-parallel-connected Au nanogaps as efficient physical reservoirs and present a promising approach for advancing PRC systems based on Au nanogaps subjected to this activation technique.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Neural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Machine Learning and ELM

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electrical Engineering and Computer Science

Tokyo University of Agriculture & Technology

Department of Electrical and Electronic Engineering

National Institute of Technology Kisarazu College

Department of Electrical Engineering, National Institute of Technology Kushiro College, 2-23-1 Otanoshike-Nishi, Kushiro, Hokkaido 084-0916, Japan

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