•연구자: 물리학과 위상원, 이윤상
•발표일: 2026.06.04
•DOI: https://doi.org/10.1002/advs.202524334
•Sangwon Wi and Yunsang Lee, Advanced Science (Q1), Volume 13, Issue 31, e24334 (2026)
•Abstract
Rapid advancements in artificial intelligence have magnified the inherent bottlenecks and energy inefficiencies of conventionalvon Neumann architecture. To address these limitations, processing information in a highly parallel, memory-integrated mannermimicking the human brain, neuromorphic devices have emerged as a cornerstone of next-generation computing. Among these,optical-neuromorphic devices are particularly promising. By using light, they offer transformative advantages, such as high speed,massive bandwidth, and minimal signal interference. Accordingly, we propose long-persistent luminescence (LPL) materials asnovel substrates for optically operative artificial synapses. We utilize AGa 2 O4 (A = Mg, Ca, Sr, or Ba) luminescent oxides in whichthe intrinsic defect states enable excellent LPL properties without complex material engineering. Leveraging these properties, wedemonstrate the physical implementation of optical material-based neural processing, in which memory retention and nonlineartransformation are executed within the LPL material. As proof of concept, this physical neural network was applied to a real-time Pong gameplay, demonstrating autonomous decision-making through light-driven signal processing. To further extend, wedeveloped physical reservoir computing and physical neural network architectures. These architectures exploit the nonlineartemporal dynamics and luminescence mapping of LPL to perform handwritten digit recognition. Our findings establish LPLmaterials as a versatile platform for developing next-generation, energy-efficient optical-neuromorphic systems.