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Adaptive infrared thermal camouflage of multi-layer PCMs devices via laser-electric co-modulation driven by neural network

  • Kailin Zhao
  • , Qin Guo
  • , Lan Jiang
  • , Yansong Zhang
  • , Shuhui Jiao
  • , Jie Hu
  • , Qian Cheng
  • , Xun Cao
  • , Weina Han*
  • *Corresponding author for this work
  • Beijing Institute of Technology
  • CAS - Shanghai Institute of Ceramics
  • University of Chinese Academy of Sciences

Research output: Contribution to journalArticlepeer-review

Abstract

Infrared thermal camouflage technologies are vital for enhancing the survivability of objects by altering their infrared radiation properties. However, existing solutions often fall short in adaptability and rapid responsiveness to dynamic environmental conditions, limiting their practical applicability. To overcome these challenges, we present an innovative approach combining ultrafast laser-induced non-volatile phase-change Ge2Sb2Te5 (GST) voxel-crystallized units with electrically tunable volatile VO2 layers. This integration enables precise, continuous control of infrared emissivity across a wide range of 0.14 to 0.98, effectively encompassing the emissivity of most materials. A neural network-based closed-loop system is employed for sensing, intelligent decision-making, and execution, achieving real-time thermal radiation matching between the target and its environment with a response speed of 3 °C/s and an accuracy of ± 1 °C. This strategy significantly enhances the adaptability of thermal camouflage in complex environments, paving the way for practical, dynamic thermal stealth applications.

Original languageEnglish
Article number42
JournalPhotoniX
Volume6
Issue number1
DOIs
Publication statusPublished - Dec 2025

Keywords

  • Adaptive thermal camouflage system
  • Emissivity
  • Laser-electric
  • Neural network
  • Phase-change materials

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