All-optical nonlinear activation function for photonic neural networks [Invited]

Abstract

With the recent successes of neural networks (NN) to perform machine-learning tasks, photonic-based NN designs may enable high throughput and low power neuromorphic compute paradigms since they bypass the parasitic charging of capacitive wires. Thus, engineering data-information processors capable of executing NN algorithms with high efficiency is of major importance for applications ranging from pattern recognition to classification. Our hypothesis is therefore, that if the time-limiting electro-optic conversion of current photonic NN designs could be postponed until the very end of the network, then the execution time of the photonic algorithm is simple the delay of the time-of-flight of photons through the NN, which is on the order of picoseconds for integrated photonics. Exploring such all-optical NN, in this work we discuss two independent approaches of implementing the optical perceptron's nonlinear activation function based on nanophotonic structures exhibiting i) induced transparency and ii) reverse saturated absorption. Our results show that the all-optical nonlinearity provides about 3 and 7 dB extinction ratio for the two systems considered, respectively, and classification accuracies of an exemplary MNIST task of 97% and near 100% are found, which rivals that of software based trained NNs, yet with ignored noise in the network. Together with a developed concept for an all-optical perceptron, these findings point to the possibility of realizing pure photonic NNs with potentially unmatched throughput and even energy consumption for next generation information processing hardware.

Misalignment resilient diffractive optical networks

“…Furthermore, deep learning and related optimization tools have been harnessed to find data-driven solutions for various inverse problems arising in, e.g., microscopy [18][19][20][21][22], nanophotonic designs and plasmonics [23][24][25]. These demonstrations and others have been motivating some of the recent advances in optical neural networks and related optical computing techniques that aim to exploit the computational speed, power-efficiency, scalability and parallelization capabilities of optics for machine intelligence applications [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

ITO-based electro-absorption modulator for photonic neural activation function

“…Different nonlinear activation functions with significantly different ranges and trends 14 have been proposed and extensively investigated, providing suitable advantages according to the different applications. More in details, interesting experimental and numerical all-optical nonlinear module based on saturable and reverse absorption 15,16 , graphene excitable lasers 17,18 , twosection distributed-feedback (DFB) lasers 19 , quantum dots 20 , disks lasers 21,22 , induced transparency in quantum assembly 15 have recently been reported and showed promising results in terms of efficiency and throughput for different kinds of neural network and applications, ranging from convolutional neural network, spiking neural network and reservoir computing. Although, a more straightforward implementation is currently attained by exploiting electro-optic tuned nonlinear materials 23,24 or absorptive modulator directly connected to a photodiode, as shown in 14,[25][26][27][28] .…”

Coupling-enhanced dual ITO layer electro-absorption modulator in silicon photonics

“…The weak EO properties of Silicon 8 , however, result in order of millimeter-to-centimeter large modulator footprints, and thus impedes large-scale integration strategies, which was a major driver for the chip industry over decades 9 . Recent explorations in using photonic integrated circuits (PIC) for the interconnectivity functions of neural network also point to the importance of densification 10,11 .…”