Threshold plasticity of SOI-GST microring resonators.

Spiking Neural Networks, also known as third generation Artificial Neural Networks, have widely attracted more attention because of their advantages of behaving more biologically interpretable and being more suitable for hardware implementation. Apart from using traditional synaptic plasticity, neural networks can also be based on threshold plasticity, achieving similar functionality. This can be implemented using e.g. the Bienenstock, Cooper and Munro rule. This is a classical unsupervised learning mechanism in which the threshold is closely related to the output of the post-synaptic neuron. We show in simulations that the threshold characteristics of the nonlinear effects of a microring resonator integrated with Ge2Sb2Te5 demonstrate some complex dependencies on the intracavity refractive index, attenuation, and wavelength detuning of the incident optical pulse, and exhibit class II excitability. We also show that we are able to modify the threshold power of the microring resonator by the changes of the refractive index and loss of Ge2Sb2Te5, due to transitions between the crystalline and amorphous states. Simulations show that the presented device exhibits both excitatory and inhibitory learning behavior, either lowering or raising the threshold.

Flexible optical fiber channel modeling based on a neural network module.

Optical fiber channel modeling, which is essential in optical transmission system simulations and designs, is usually based on the split-step Fourier method (SSFM), making the simulation quite time-consuming owing to the iteration steps. Here, we train a neural network module termed NNSpan to learn the transfer function of a single fiber (G652 or G655) span with a length of 80 km and successfully emulate long-haul optical transmission systems by cascading multiple NNSpans, which gives remarkable prediction accuracy, even over a transmission distance of 1000 km. Even when trained without erbium-doped fiber amplifier (EDFA) noise, NNSpan performs quite well when emulating the systems affected by EDFA noise. An optical bandpass filter can optionally be added after EDFA, making the simulation more flexible. Comparison with the SSFM shows that NNSpan has a distinct computational advantage, with the computation time reduced by a factor of 12. This method based on NNSpan could be a supplementary option for optical transmission system simulations, thus contributing to system designs as well.

Programmable low-power consumption all-optical nonlinear activation functions using a micro-ring resonator with phase-change materials.

A programmable hardware implementation of all-optical nonlinear activation functions for different scenarios and applications in all-optical neural networks is essential. We demonstrate a programmable, low-loss all-optical activation function device based on a silicon micro-ring resonator loaded with phase change materials. Four different nonlinear activation functions of Relu, ELU, Softplus and radial basis functions are implemented for incident signal light of the same wavelength. The maximum power consumption required to switch between the four different nonlinear activation functions in calculation is only 1.748 nJ. The simulation of classification of hand-written digit images also shows that they can perform well as alternative nonlinear activation functions. The device we design can serve as nonlinear units in photonic neural networks, while its nonlinear transfer function can be flexibly programmed to optimize the performance of different neuromorphic tasks.