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
20202020 International Applied Computational Electromagnetics Society Symposium (ACES)41 citationsDOI
Abstract
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.
Topics & Concepts
PhotonicsComputer scienceRobustness (evolution)Artificial neural networkElectronic engineeringNeuromorphic engineeringArtificial intelligenceMaterials scienceEngineeringOptoelectronicsGeneBiochemistryChemistryNeural Networks and Reservoir ComputingPhotonic and Optical DevicesPhase-change materials and chalcogenides