Abstract
Recently, integrated optics has become a functional platform for implementing machine learning algorithms and, in particular, neural networks. Photonic integrated circuits can straightforwardly perform vector-matrix multiplications with high efficiency and low power consumption by using weighting mechanism through linear optics. However, this cannot be said for the activation function, i.e., “threshold,” 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 electroabsorption modulator based on an indium tin oxide layer monolithically integrated into silicon photonic waveguides, whose dynamic range is used as a nonlinear activation function of a photonic neuron. The thresholding mechanism is based on a photodiode, which integrates the weighed products, and whose photovoltage drives the electroabsorption 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.
Highlights
With the ongoing advancement of neural network systems, there is a pressing demand for new technological paradigms that can perform advanced artificial intelligence tasks without trading off throughput and power dissipation
An activation function derived from Eq (12) for both Indium Tin Oxide (ITO) and Si based absorption modulators coupled to a photodiode [Fig. 2(c)] through an ideal transimpedance amplifier was trained in Keras67 at a simulated bandwidth of 1 GHz
We keep the scaling scitation.org/journal/apm independent of the modal structure choice by using the effective thickness as the underlying unit for the device length as teff can be different for different modes but the physical length required for modulation relates back to teff based on the modal choice as L/teff depends only on material constraints
Summary
With the ongoing advancement of neural network systems, there is a pressing demand for new technological paradigms that can perform advanced artificial intelligence tasks without trading off throughput (operations/s) and power dissipation. The photogenerated current, proportional to the detected optical power at the weighted addition, alters the voltage drop on the active material, changing its carrier concentration and the effective modal index of the propagating waveguide mode This approach is affected by RC-latency and by the electro-optic conversion; it trades off baud-rate with energy efficiency.. Current schemes consist of using subdiffraction limited plasmonic structures or photonic cavities aiming to maximize the light matter interaction in order to achieve a rather high modulation performance and low energyper-compute surpassing electronic efficiency, while compensating in terms of insertion losses (optical mode hybridization) due to the plasmonic nature of the mode Another characteristic to consider when engineering an electroabsorption (EA) modulator is the modulation bandwidth, which is the result of material choices and device configuration and, if well engineered, can enable high-throughput communication links. The main aspect to consider when designing and engineering an effective electroabsorption modulator (EAM) is, the variation of the complex refractive index due to applied bias (i.e., carrier tunability), which is inherent to the selected active material. Silicon (Si) is the conventional material choice usually as fabrication facilities can benefit tremendously from the mature Si process, but the inherent low tunability of Si under electrical bias forces inadequate performances at higher scaling as increased modulator lengths need to be employed to achieve the desired dynamic range for the NL AF
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