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Force-Triggered Non-Volatile Multilevel Mechano-Optical Memory System for Logic Computation and Image Recognition.

Jiaxing Guo1, Feng Guo2, Hang Yang3

  • 1Institute of Modern Optics and Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin, 300071, P. R. China.

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This study introduces a novel non-volatile, multilevel mechano-optical memory system. This system integrates mechanoluminescence (ML) and photostimulated luminescence (PSL) for advanced AI applications, enabling computation without electrical interference.

Keywords:
boolean logic operationsin‐memory computingmechanoluminescencemechano‐optical memoryphotostimulated luminescence

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Area of Science:

  • Materials Science
  • Optoelectronics
  • Artificial Intelligence Hardware

Background:

  • The demand for efficient data processing in the big data era necessitates overcoming the limitations of traditional computer architectures.
  • Mechanoluminescence (ML) materials offer unique capabilities for sensing mechanical forces without external power, but their non-volatile memory applications are limited by instantaneous luminescence.
  • Developing non-volatile memory is crucial for sophisticated artificial intelligence (AI) applications requiring persistent data storage.

Purpose of the Study:

  • To propose and demonstrate a non-volatile, multilevel mechano-optical memory system.
  • To integrate mechanoluminescence (ML) with photostimulated luminescence (PSL) for enhanced memory capabilities.
  • To explore the potential of this system for computational tasks and neuromorphic applications.

Main Methods:

  • Fabrication of a memory system combining a self-recoverable ML material (ZnS:Cu) and a PSL phosphor (CaSrS:Eu).
  • Utilizing the combined ML and PSL effects to achieve a six-level non-volatile memory.
  • Implementing Boolean logic operations and neuromorphic visual pattern pre-processing using the developed memory system.

Main Results:

  • A robust six-level non-volatile mechano-optical memory was successfully achieved.
  • The system demonstrated reliable multilevel memory states enabling computational capabilities without electrical interference.
  • Neuromorphic visual pattern pre-processing significantly improved recognition accuracy from 20% to 80%.

Conclusions:

  • The integration of ML and PSL effects provides a new strategy for developing mechano-optical memory devices.
  • The developed system offers a promising platform for future intelligent applications, including advanced AI and neuromorphic computing.
  • Force-responsive phosphors can be endowed with memory capabilities, paving the way for novel hardware concepts.