Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory
Mario Miscuglio, Jiawei Meng, Omer Yesiliurt, Yifei Zhang, Ludmila J. Prokopeva, Armin Mehrabian, Juejun Hu, Alexander V. Kildishev, Volker J. Sorger · 2020 International Applied Computational Electromagnetics Society Symposium (ACES) · 2020
Here we present a multi-level discrete-state nonvolatile photonic memory based on an ultra-compact ( ) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. Emulating an opportunely trained 100×100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 92% inference accuracy and robustness towards noise when performing predictions of unseen data.