Experimental phase-error extraction and modelling in silicon photonic arrayed waveguide gratings

We present a detailed study of parameter sweeps of silicon photonic arrayed waveguide gratings (AWG), looking into the effects of phase errors in the delay lines, which are induced by fabrication variation. We fabricated AWGs with 8 wavelength channels spaced 200 GHz and 400 GHz apart. We swept the waveguide width of the delay lines, and also performed a sweep where we introduced increments of length to the waveguides to emulate different AWG layouts and look into the effect of the phase errors. With this more detailed study we could quantitatively confirm the results of earlier studies, showing the wider waveguides reduce the effect of phase errors and dramatically improve the performance of the AWGs in terms of insertion loss and crosstalk. We also looked into the effect of rotating the layout of the circuit on the mask, and here we could show that, contrary to results with older technologies, this no longer has an effect on the current generation of devices

Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing

Photonic reservoir computing is a hardware implementation of the concept of reservoir computing which comes from the field of machine learning and artificial neural networks. This concept is very useful for solving all kinds of classification and recognition problems. Examples are time series prediction, speech and image recognition. Reservoir computing often competes with the state-of-the-art. Dedicated photonic hardware would offer advantages in speed and power consumption. We show that a network of coupled semiconductor optical amplifiers can be used as a reservoir by using it on a benchmark isolated words recognition task. The results are comparable to existing software implementations and fabrication tolerances can actually improve the robustness

Photonic reservoir computing: a new approach to optical information processing

Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently advances have been made by the introduction of the new concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks and has been successfully used in several pattern classification problems, like speech and image recognition. The implementations have so far been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it on a benchmark speech recognition task to conventional software implementations. In spite of several differences, they perform as good as or better than conventional implementations. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We will also address the role phase plays on the reservoir performance

Self-pulsing and chaos in short chains of coupled nonlinear microcavities

We examine time-domain instabilities in short chains of coupled Kerr-nonlinear resonators. We show that a large parameter region of self-pulsing or chaotic behavior can be realized. We give a detailed description of the possible states in systems with two and three cavities, using phenomenological coupled-mode equations and rigorous simulations. A particular geometry can exhibit a rich range of dynamics, dependent on input conditions. A clear link with the linear transmission properties is shown. This system complements the studies of the nonlinear Bragg reflector and electrons in a nonlinear lattice. Unlike the Bragg grating case, we observe wide detuning ranges that exhibit self-pulsing without first going through a bistable transition

Python bindings for the open source electromagnetic simulator Meep

Meep is a broadly used open source package for finite-difference time-domain electromagnetic simulations. Python bindings for Meep make it easier to use for researchers and open promising opportunities for integration with other packages in the Python ecosystem. As this project shows, implementing Python-Meep offers benefits for specific disciplines and for the wider research community

Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns

Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)

Advances in photonic reservoir computing on an integrated platform

Reservoir computing is a recent approach from the fields of machine learning and artificial neural networks to solve a broad class of complex classification and recognition problems such as speech and image recognition. As is typical for methods from these fields, it involves systems that were trained based on examples, instead of using an algorithmic approach. It originated as a new training technique for recurrent neural networks where the network is split in a reservoir that does the `computation' and a simple readout function. This technique has been among the state-of-the-art. So far implementations have been mainly software based, but a hardware implementation offers the promise of being low-power and fast. We previously demonstrated with simulations that a network of coupled semiconductor optical amplifiers could also be used for this purpose on a simple classification task. This paper discusses two new developments. First of all, we identified the delay in between the nodes as the most important design parameter using an amplifier reservoir on an isolated digit recognition task and show that when optimized and combined with coherence it even yields better results than classical hyperbolic tangent reservoirs. Second we will discuss the recent advances in photonic reservoir computing with the use of resonator structures such as photonic crystal cavities and ring resonators. Using a network of resonators, feedback of the output to the network, and an appropriate learning rule, periodic signals can be generated in the optical domain. With the right parameters, these resonant structures can also exhibit spiking behaviour