On-chip photonic synapse
By Zengguang Cheng, Carlos Ríos, Wolfram H. P. Pernice, C. D. Wright, Harish Bhaskaran. Science Advances, 2017
494 citations recorded by the source corpus.
An on-chip photonic device mimics the function of synaptic connections between neurons. The search for new “neuromorphic computing” architectures that mimic the brain’s approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/c2fa18dc259bd726a2eed205e7df516a1087397b
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In Cellular and Molecular Neuroscience
In Cognitive Neuroscience
In Electrical and Electronic Engineering
Citations in the corpus
- An on-chip photonic deep neural network for image classification (2021, background): “Recently, photonic implementations of deep neural networks have been reported 15-17 , 23-30 Here we report the demonstration of the first integrated end-to-end photonic deep neural network that utilises computation-by-propagation to perform instantaneous image classification.”
- All-optical spiking neurosynaptic networks with self-learning capabilities (2019, methodology): “…photonic neuromorphic computing is however that based around phase-change materials and devices, since these have been shown to exhibit an intrinsic ability to provide in hardware the basic integrate-and-fire functionality of neurons and the plastic weighting operation of synapses 14 15 16 17 18 .”