Ultrasensitive Pa‐Level Persistent Mechanoluminescent Material Toward All‐Optical Neural Synapses for Tactile‐Visual Information Recognition and Memory

Abstract

Mechanoluminescence (ML), a self‐recovering and passive luminescent modality, offers a promising path toward tactile‐visual all‐optical neuromorphic computing, potentially overcoming the inefficiency of von Neumann architecture. However, existing ML materials are hindered by high response thresholds and single‐mode luminescence, preventing sub‐kPa perception and multilevel neural transmission. Here, we employ Li+/Dy3+ co‐doping in Sr2SiO4:Eu2+ (LSSO) to implement a defect engineering strategy that synergistically optimizes oxygen vacancies and suppresses strontium vacancies, achieving dual breakthroughs in sensitivity and signal clarity. This approach yields a record‐low ML threshold of 72 Pa—the only Pa‐level system achieved without external electricity or elastomeric structural modifications. This material also responds to sunlight, force, and heat, emulating diverse synaptic functions like tactile/optic nerve perception, short‐term potentiation, and memory. It exhibits a 7‐s persistent ML with a signal‐to‐noise ratio of 20.57 which is 15.6 times higher than commercial SrAl2O4:Eu2+,Dy3+, a micron‐scale imaging resolution (≈200 µm), and a 36‐hour memory capacity. These properties enable thermal‐activated information awakening and visual imaging over 1000 cycles, with a memory accuracy 209% superior to the Ebbinghaus curve. This work not only advances the design of all‐optical synapses but also forges a pivotal connection between ML and neuromorphic engineering, propelling energy‐efficient, light‐driven artificial intelligence.