Affiliation:
1. Electronics and Nanoscale Engineering James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UK
2. Department of Materials Science and Engineering City University of Hong Kong Kowloon Tong Hong Kong
3. School of Science and Technology Hong Kong Metropolitan University Ho Man Tin Hong Kong
Abstract
In the realm of artificial intelligence, ultrahigh‐performance neuromorphic computing plays a significant role in executing multiple complex operations in parallel while adhering to a more biologically plausible model. Despite their importance, developing an artificial synaptic device to match the human brain's efficiency is an extremely complex task involving high energy consumption and poor parallel processing latency. Herein, a simple molecule, copper‐iodide‐based artificial synaptic device demonstrating core synaptic functions of human neural networks is introduced. Exceptionally high carrier mobility and dielectric constant in the developed device lead to superior efficacies in neuromorphic characteristics with ultrahigh paired‐pusle facilitation index (>195). The results demonstrate biomimetic capabilities that exert a direct influence on neural networks across multiple timescales, ranging from short‐ to long‐term memory. This flexible reconfiguration of neural excitability provided by the copper‐iodide‐based synaptic device positions it as a promising candidate for creating advanced artificial intelligence systems.
Funder
Engineering and Physical Sciences Research Council
Subject
Condensed Matter Physics,General Materials Science