Organic Optoelectronic Memristor Inspired by Insect Ultraviolet Vision for Attention Mechanism Simulation

The rapid evolution of modern technology is propelling artificial intelligence into a transformative phase, where visual information processing has become a central focus. The development of new artificial optoelectronic synapses integrating sensing and preprocessing functions represents a transformative approach to enhancing machine vision systems’ response capabilities. Inspired by the human retina system, a novel polymer with dual optoelectronic response and adjustable conductivity, poly[1,2-bis(2-methyl-5-phenylthiophen-3-yl)cyclopent-1-ene- alt -1,1’-((9 H -fluorene-9,9-diyl)bis(hexane-6,1-diyl))-bis(4-ferrocene-1 H -1,2,3-triazole)] (PTH-Fc), is synthesized. The as-fabricated Al/PTH-Fc/ITO device exhibited outstanding history-dependent memristive performance with robust retention characteristics and higher device yield. For PTH-Fc, the diarylethenes unit is the core for achieving the function of light-triggered synaptic behavior, while the ferrocene unit is the key for realizing the analog and multilevel “electric modulation” function under an electric field. The multifunctional synaptic behavior exhibited by the PTH-Fc-based device thus stems from the synergistic mechanism of these two components: UV light first drives the cycloisomerization of the diarylethene backbone, forming an initial state with enhanced conjugation. This state not only directly contributes to the photocurrent but also optimizes the local electronic environment, allowing the adjacent ferrocene units to undergo more efficient and stable reversible redox reactions under the drive of an electric field (or photogenerated electric field), thereby achieving fine and persistent modulation of the conductance state. This work not only demonstrates the feasibility of optoelectronic synergy regulation in simulating neural functions but also provides new materials and new paradigms for the development of artificial optoelectronic synapses for machine vision.