Design and fabrication of optical electroabsorption modulator for high speed and high efficiency

Chiu, Yi‐Jen

doi:10.1109/icocn.2015.7203651

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Photonics based artificial neurons can potentially provide the archetypal solution for this specific challenge. [1][2][3] The main advantage of photonics over current digital electronics implementations is that distinct signals due to their wave-nature can be straightforwardly and efficiently combined exploiting attojoule efficient electro-optic modulators [4][5][6][7] , phase shifters and combiners, simplifying essential operations such as weighted sum or addition, vector matrix multiplications or convolutions. 8 Moreover, photonics enables high parallelism 9 , hence a higher baud-rate, since multiple wavelengths can travel in the same physical channel by exploiting wavelength-division multiplexing (WDM) 8,[10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Recently integrated optics has become an intriguing platform for implementing machine learning algorithms and in particular neural networks. Integrated photonic circuits can straightforwardly perform vector-matrix multiplications with high efficiency and low power consumption by using weighting mechanism through linear optics. Although, this can not be said for the activation function which requires either nonlinear optics or an electro-optic module with an appropriate dynamic range. Even though all-optical nonlinear optics is potentially faster, its current integration is challenging and is rather inefficient. Here we demonstrate an electro-absorption modulator based on an Indium Tin Oxide layer, whose dynamic range is used as nonlinear activation function of a photonic neuron. The nonlinear activation mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drives the elecro-absorption modulator. The synapse and neuron circuit is then constructed to execute a 200-node MNIST classification neural network used for benchmarking the nonlinear activation function and compared with an equivalent electronic module.

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