ITO-based Electro-absorption Modulator for Photonic Neural Activation Function
Year: 2023
Type: preprint
Abstract: Recently integrated optics has become an intriguing platform for implementing machine learning algorithms and inparticular neural networks. Integrated photonic circuits can straightforwardly perform vector-matrix multiplicationswith 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 withan appropriate dynamic range. Even though all-optical nonlinear optics is potentially faster, its current integrationis challenging and is rather inefficient. Here we demonstrate an electro-absorption modulator based on an IndiumTin Oxide layer, whose dynamic range is used as nonlinear activation function of a photonic neuron. The nonlinearactivation mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drivesthe elecro-absorption modulator. The synapse and neuron circuit is then constructed to execute a 200-node MNISTclassification neural network used for benchmarking the nonlinear activation function and compared with an equivalentelectronic module.
Source: Figshare
Authors: Rubab Amin (4911166), Jonathan George (2394445), Shuai Sun (775057), Thomas Ferreira de Lima (12421777), Alexander N. Tait (14405889), Jacob Khurgin (4911163), Mario Miscuglio (3330321), Bhavin J. Shastri (14356401), Paul. R. Prucnal (17508240), Tarek El-Ghazawi (14417844), Volker J. Sorger (1409038)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4286715062