Force‐Triggered Non‐Volatile Multilevel Mechano‐Optical Memory System for Logic Computation and Image Recognition - Jiaxing Guo; Feng Guo; Hang Yang; Tianhong Zhou; Xiaona Du; Rui Gao; Haisheng Chen; Minghao Hu; Weiwei Liu; Yang Zhang; Dong Tu; Jianhua Hao (Advanced Science)
Abstract In the big data era, sensing multi‐modal information in memory is highly demanded for the sake of artificial intelligence applications to overcome the limitations of the von Neumann architecture. Different from traditional sensing methodologies, mechanoluminescence (ML) materials, which emit light in response to mechanical force without any external power supply, present intriguing prospects for technological developments. However, most of the ML materials only demonstrate instantaneous luminescence, severely hampering the exploitation of ML in sophisticated applications where non‐volatile control is indispensable. Herein, a non‐volatile, multilevel mechano‐optical memory system is proposed, based on a crafted combination of a self‐recoverable ML material, ZnS:Cu, and a photostimulated luminescence (PSL) phosphor Ca0.25Sr0.75S:Eu (CaSrS:Eu). By integrating ML with PSL effect, a robust six‐level non‐volatile memory is achieved, in which the multilevel memory states allow for computational capability without electrical interference. Specifically, the reliable multilevel and non‐volatile response enables Boolean logic operations. Furthermore, neuromorphic visual pattern pre‐processing is implemented, resulting in a substantial increase in recognition accuracy from 20% to 80%. These findings endow force‐responsive phosphors with memory capability, fully leveraging the capabilities of ML and offering a new strategy for developing mechano‐optical hardware and concepts for future intelligent applications.