Martin Fiers

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.

Design Challenges in Silicon Photonics

Silicon photonics is rapidly gaining maturity in high-bandwidth optical communication, with applications in datacom, access networks, and I/O for bandwidth-intensive electronics. Also, applications are emerging in spectroscopy and sensing. To get the best performance out of the photonics, co-integration with electronics is needed: side-by-side, stacked, or on the same chip. However, the combination of photonics and electronics introduces a range of new problems on the design side: Codesign and cosimulation of complex photonic and electronic circuits, tolerance to variability, and verification algorithms that can handle photonic circuits. We will discuss these challenges and give an outlook on how tools need to evolve to address the needs of photonic-electronic IC designers.

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.

Improving the design cycle for nanophotonic components

Abstract We present IPKISS, a software framework that greatly simplifies the design of nanophotonic components. In this approach, all steps in the workflow are based on a single high-level definition of the component, in a Python script. Because there is only one description, the design flow becomes less error prone due to incorrect definitions, and the overall reproducibility is greatly improved. Furthermore it enables easy closed-loop modeling of components and circuits. Also, previous work can easily be built upon because lower level blocks can seamlessly be replaced by new blocks. While we illustrate the application in photonics, this software and the used design patterns can be extended to other domains such as RF design and to multidomain physics such as opto-electronics.

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).

Python Bindings for the Open Source Electromagnetic Simulator Meep

This paper describes Meep, a popular free implementation of the finite-difference time-domain (FDTD) method for simulating electromagnetism. In particular, we focus on aspects of implementing a full-featured FDTD package that go beyond standard textbook descriptions of the algorithm, or ways in which Meep differs from typical FDTD implementations. These include pervasive interpolation and accurate modeling of subpixel features, advanced signal processing, support for nonlinear materials via Padé approximants, and flexible scripting capabilities.

IPKISS: A parametric design and simulation framework for silicon photonics

We present IPKISS, an open-source design environment for complex (silicon) photonic circuitry. The tool set allows flexible mask design, direct electromagnetic simulation and circuit models, and can be easily interfaced with existing tools.

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).