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 Ge2 Sb2 Sex Te5-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., Ge2 Sb2 Se3 Te2 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.

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