TY - JOUR
T1 - Artificial Biphasic Synapses Based on Nonvolatile Phase-Change Photonic Memory Cells
AU - Zhou, Wen
AU - Farmakidis, Nikolaos
AU - Li, Xuan
AU - Tan, James
AU - Aggarwal, Samarth
AU - Feldmann, Johannes
AU - Brückerhoff-Plückelmann, Frank
AU - David Wright, C.
AU - Pernice, Wolfram H.P.
AU - Bhaskaran, Harish
N1 - Publisher Copyright:
© 2022 The Authors. physica status solidi (RRL) Rapid Research Letters published by Wiley-VCH GmbH.
PY - 2022/9
Y1 - 2022/9
N2 - 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 Ag3In4Sb76Te17 (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.
AB - 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 Ag3In4Sb76Te17 (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.
KW - artificial synapses
KW - neuromorphic photonics
KW - nonvolatile photonic memory
KW - phase-change materials
UR - https://www.scopus.com/pages/publications/85128817740
U2 - 10.1002/pssr.202100487
DO - 10.1002/pssr.202100487
M3 - 文章
AN - SCOPUS:85128817740
SN - 1862-6254
VL - 16
JO - Physica Status Solidi - Rapid Research Letters
JF - Physica Status Solidi - Rapid Research Letters
IS - 9
M1 - 2100487
ER -