Litcius/Paper detail

Artificial Biphasic Synapses Based on Nonvolatile Phase‐Change Photonic Memory Cells

Wen Zhou, Nikolaos Farmakidis, Xuan Li, James Tan, Samarth Aggarwal, Johannes Feldmann, Frank Brückerhoff‐Plückelmann, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran

2022physica status solidi (RRL) - Rapid Research Letters19 citationsDOIOpen Access PDF

Abstract

Nonvolatile photonic memory cells are basic building blocks for neuromorphic hardware enabling the realization of all‐optical synapses and artificial neurons. These devices commonly exploit chalcogenide phase‐change materials, which are evanescently coupled to photonic waveguides, and provide fast write/erase speeds and large storage capacity. Here, we report for the first time the programming of a nonvolatile photonic memory cell based on Ag 3 In 4 Sb 76 Te 17 (AIST) which is capable of mimicking biphasic synapses. We evaluate the underlying mechanism of biphasic behavior of AIST cells based on numerical simulations and correlate to experimental findings. Switching dynamics demonstrate enhanced performance with a post‐excitation dead time as short as 12.8 ns. Based on AIST double cells, we demonstrate reversible multilevel switching between 45 unique synaptic weights for long‐term depression (LTD) and long‐term potentiation (LTP). The observed biphasic programming and excellent switching performance render AIST‐based photonic memory cells promising for artificial neural networks and neuromorphic photonic computing hardware.

Topics & Concepts

Neuromorphic engineeringPhotonicsComputer sciencePhase-change memoryRealization (probability)Materials scienceOptical switchArtificial neural networkOptoelectronicsNanotechnologyArtificial intelligenceStatisticsMathematicsLayer (electronics)Neural Networks and Reservoir ComputingAdvanced Memory and Neural ComputingPhase-change materials and chalcogenides