Neuromorphic photonics with electro-absorption modulators

Jonathan K. George, Armin Mehrabian, Rubab Amin, Jiawei Meng, Thomas Ferreira De Lima, Alexander N. Tait, Bhavin J. Shastri, Tarek El-Ghazawi, Paul R. Prucnal, Volker J. Sorger

Research output: Contribution to journalArticlepeer-review

78 Scopus citations


Photonic neural networks benefit from both the high-channel capacity and the wave nature of light acting as an effective weighting mechanism through linear optics. Incorporating a nonlinear activation function by using active integrated photonic components allows neural networks with multiple layers to be built monolithically, eliminating the need for energy and latency costs due to external conversion. Interferometer-based modulators, while popular in communications, have been shown to require more area than absorption-based modulators, resulting in a reduced neural network density. Here, we develop a model for absorption modulators in an electro-optic fully connected neural network, including noise, and compare the network's performance with the activation functions produced intrinsically by five types of absorption modulators. Our results show the quantum well absorption modulator-based electro-optic neuron has the best performance allowing for 96% prediction accuracy with 1:7×10 -12 J/MAC excluding laser power when performing MNIST classification in a 2 hidden layer feed-forward photonic neural network.

Original languageEnglish (US)
Pages (from-to)5181-5191
Number of pages11
JournalOptics Express
Issue number4
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics


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