Multiscale simulations of Ge–Sb–Se–Te phase-change alloys for photonic memory applications

Phase-change materials (PCMs) are among the most promising candidates for next-generation non-volatile memory and neuromorphic computing technologies. However, their photonic applications are hindered by a trade-off between refractive index contrast and optical absorption losses. Artificial intelligence-assisted computational approaches are essential for fundamental understanding and device modeling of PCMs. In this work, we systematically investigate structural and optical properties of crystalline and amorphous Ge2Sb2SexTe5-x (x = 0 to 4) alloys using density functional theory (DFT), and then use the DFT-computed optical parameters for modeling and optimization of photonic computing devices via the finite-difference time-domain method. Among the investigated compositions, we identify a promising candidate, i.e., Ge2Sb2Se3Te2 for all-optical switching on a silicon-on-insulator (SOI) platform. Finally, we design a dual-disk PCM waveguide structure on SOI with an enhanced switching contrast and a low optical loss for scalable photonic neural network application.