Thomas Van Vaerenbergh

Experimental demonstration of reservoir computing on a silicon photonics chip

In todays age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s(-1), without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds >100 Gbit s(-1). These results pave the way for the application of integrated photonic RC for a wide range of applications.

Cascadable excitability in microrings

To emulate a spiking neuron, a photonic component needs to be excitable. In this paper, we theoretically simulate and experimentally demonstrate cascadable excitability near a self-pulsation regime in high-Q-factor silicon-on-insulator microrings. For the theoretical study we use Coupled Mode Theory. While neglecting the fast energy and phase dynamics of the cavity light, we can still preserve the most important microring dynamics, by only keeping the temperature difference with the surroundings and the amount of free carriers as dynamical variables of the system. Therefore we can analyse the microring dynamics in a 2D phase portrait. For some wavelengths, when changing the input power, the microring undergoes a subcritical Andronov-Hopf bifurcation at the self-pulsation onset. As a consequence the system shows class II excitability. Experimental single ring excitability and self-pulsation behaviour follows the theoretic predictions. Moreover, simulations and experiments show that this excitation mechanism is cascadable.

Time-domain and frequency-domain modeling of nonlinear optical components at the circuit-level using a node-based approach

We present a tool that aids in the modeling of optical circuits, both in the frequency and in the time domain. The tool is based on the definition of a node, which can have both an instantaneous input-output relation and different state variables (e.g., temperature and carrier density) and differential equations for these states. Furthermore, each node has access to part of its input history, allowing the creation of delay lines or digital filters. Additionally, a node can contain subnodes, allowing the creation of hierarchical networks. This tool can be used in numerous applications such as frequency-domain analysis of optical ring filters, time-domain analysis of optical amplifiers, microdisks, and microcavities. Although we mainly use this tool to model optical circuits, it can also be used to model other classes of dynamical systems, such as electrical circuits and neural networks.

Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response

We demonstrate class I excitability in optically injected microdisk lasers, and propose a possible optical spiking neuron design. The neuron has a clear threshold and an integrating behavior, leading to an output rate-input rate dependency that is comparable to the characteristic of sigmoidal artificial neurons. We also show that the optical phase of the input pulses has influence on the neuron response, and can be used to create inhibitory, as well as excitatory perturbations.

Excitation transfer between optically injected microdisk lasers

Recently, we have theoretically demonstrated that optically injected microdisk lasers can be tuned in a class I excitable regime, where they are sensitive to both inhibitory and excitatory external input pulses. In this paper, we propose, using simulations, a topology that allows the disks to react on excitations from other disks. Phase tuning of the intermediate connections allows to control the disk response. Additionally, we investigate the sensitivity of the disk circuit to deviations in driving current and locking signal wavelength detuning. Using state-of-the-art fabrication techniques for microdisk laser, the standard deviation of the lasing wavelength is still about one order of magnitude too large. Therefore, compensation techniques, such as wavelength tuning by heating, are necessary.

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).

Trainable hardware for dynamical computing using error backpropagation through physical media

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation—a crucial step for tuning such systems towards a specific task—can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Experimental Extraction of Effective Refractive Index and Thermo-Optic Coefficients of Silicon-on-Insulator Waveguides Using Interferometers

We propose and demonstrate an accurate method of measuring the effective refractive index and thermo-optic coefficient of silicon-on-insulator waveguides in the entire C-band using three Mach-Zehnder interferometers. The method allows for accurate extraction of the wavelength dispersion and takes into account fabrication variability. Wafer scale measurements are performed and the effective refractive index variations are presented for three different waveguide widths: 450, 600, and 800 nm, for the TE polarization. The presented method is generic and can be applied to other waveguide geometries and material systems and for different wavelengths and polarizations.

CAPHE: Time-domain and Frequency-domain Modeling of Nonlinear Optical Components

We present CAPHE, a tool for modeling optical circuits in time and frequency domain. Some applications are optical filter design, variational studies and dynamical modeling of strongly nonlinear components (microrings, microdisks, SOAs).

Measurements of Effective Refractive Index of SOI Waveguides using Interferometers

We demonstrate an accurate method of measuring the effective refractive index of SOI waveguides in the C-band using three Mach-Zehnder Interferometers. Over wafer the average extraction error of effective index and group index is 0.003 and 0.004.