Artificial Biphasic Synapses Based on Nonvolatile Phase‐Change Photonic Memory Cells. Issue 9 (27th April 2022) - Zhou, Wen; Farmakidis, Nikolaos; Li, Xuan; Tan, James; Aggarwal, Samarth; Feldmann, Johannes; Brückerhoff-Plückelmann, Frank; David Wright, C.; Pernice, Wolfram H. P.; Bhaskaran, Harish
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 Ag3 In4 Sb76 Te17 (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. Abstract : It is the first mimicking of biological biphasic synapses using a nonvolatile and intermediate‐state Ag3 In4 Sb76 Te17 (AIST) waveguide memory cell. Long‐term depression and long‐term potentiation are respectively achieved by fast recrystallization and amorphization processes of intermediate‐state AIST cells. Their synaptic weight setting shows fast and multilevel operation with high optical programming precision.