On the modeling of thermal and free carrier nonlinearities in silicon-on-insulator microring resonators

Exploring the potential of self-pulsing optical microresonators for spiking neural networks and sensing

Photonic platforms are promising for implementing neuromorphic hardware due to their high processing speed, low power consumption, and ability to perform parallel processing. A ubiquitous device in integrated photonics, which has been extensively employed for the realization of optical neuromorphic hardware, is the microresonator. The ability of CMOS-compatible silicon microring resonators to store energy enhances the nonlinear interaction between light and matter, enabling energy efficient nonlinearity, fading memory and the generation of spikes via self-pulsing. In the self-pulsing regime, a constant input signal can be transformed into a time-dependent signal based on pulse sequences. Previous research has shown that self-pulsing enables the microresonator to function as an energy-efficient artificial spiking neuron. Here, we extend the experimental study of single and coupled microresonators in the self-pulsing regime to confirm their potential as building blocks for scalable photonic spiking neural networks. Furthermore, we demonstrate their potential for introducing all-optical long-term memory and event detection capabilities into integrated photonic neural networks. In particular, we show all-optical long-term memory up to at least 10 μs and detection of input spike rates, which is encoded into different stable self-pulsing dynamics.

Demonstration of high-frequency self-pulsing oscillations in an active silicon micro-ring cavity

We experimentally investigated the self-pulsing (SP) oscillations induced by the thermo-optic, free carrier, and Kerr nonlinear effects in integrated active silicon microring resonators. We demonstrate high frequency self-pulsing oscillations (up to 30 MHz) by applying a few millivolts of reverse bias voltage to the PIN junction of the active cavity. We illustrate that the shape of those oscillations (i.e., frequency and duty cycle) can be controlled by adjusting the CW input power and applying a reverse bias voltage to the PIN junction for carrier removal. This controlling is important for synchronizing the cavity which is crucial for neural network applications. Furthermore, we utilize a mathematical model for visualizing the stability regions by numerically studying coupled mode theory in silicon microcavity under different conditions.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

Time delay reservoir computing with a silicon microring resonator and a fiber-based optical feedback loop

Silicon microring resonators serve as critical components in integrated photonic neural network implementations, owing to their compact footprint, compatibility with CMOS technology, and passive nonlinear dynamics. Recent advancements have leveraged their filtering properties as weighting functions, and their nonlinear dynamics as activation functions with spiking capabilities. In this work, we investigate experimentally the linear and nonlinear dynamics of microring resonators for time delay reservoir computing, by introducing an external optical feedback loop. After effectively mitigating the impact of environmental noise on the fiber-based feedback phase dependencies, we evaluate the computational capacity of this system by assessing its performance across various benchmark tasks at a bit rate of few Mbps. We show that the additional memory provided by the optical feedback is necessary to achieve error-free operation in delayed-boolean tasks that require up to 3 bits of memory. In this case the microring was operated in the linear regime and the photodetection was the nonlinear activation function. We also show that the Santa Fe and Mackey Glass prediction tasks are solved when the microring nonlinearities are activated. Notably, our study reveals competitive outcomes even when employing only 7 virtual nodes within our photonic reservoir. Our findings illustrate the silicon microring's versatile performance in the presence of optical feedback, highlighting its ability to be tailored for various computing applications.

Photonic delay reservoir computer based on ring resonator for reconfigurable microwave waveform generator

To achieve an autonomously controlled reconfigurable microwave waveform generator, this study proposes and demonstrates a self-adjusting synthesis method based on a photonic delay reservoir computer with ring resonator. The proposed design exploits the ring resonator to configure the reservoir, facilitating a nonlinear transformation and providing delay space. A theoretical analysis is conducted to explain how this configuration addresses the challenges of microwave waveform generation. Considering the generalization performance of waveform generation, the simulations demonstrate the system's capability to produce six distinct representative waveforms, all exhibiting a highly impressive root mean square error (RMSE) of less than 1%. To further optimize the system's flexibility and accuracy, we explore the application of various artificial intelligence algorithms at the reservoir computer's output layer. Furthermore, our investigation delves deeply into the complexities of system performance, specifically exploring the influence of reservoir neurons and micro-ring resonator parameters on calculation performance. We also delve into the scalability of reservoirs, considering both parallel and cascaded arrangements.

Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing

Microring resonators (MRRs) are promising devices for time-delay photonic reservoir computing, but the impact of the different physical effects taking place in the MRRs on the reservoir computing performance is yet to be fully understood. We numerically analyze the impact of linear losses as well as thermo-optic and free-carrier effects relaxation times on the prediction error of the time-series task NARMA-10. We demonstrate the existence of three regions, defined by the input power and the frequency detuning between the optical source and the microring resonance, that reveal the cavity transition from linear to nonlinear regimes. One of these regions offers very low error in time-series prediction under relatively low input power and number of nodes while the other regions either lack nonlinearity or become unstable. This study provides insight into the design of the MRR and the optimization of its physical properties for improving the prediction performance of time-delay reservoir computing.

Silicon multi-mode micro-ring modulator for improved robustness to optical nonlinearity

Due to the resonant nature and silicon's strong optical nonlinearity, the system's performance of silicon micro-ring modulators can be seriously affected by the input optical power. In this Letter, we proposed and experimentally demonstrated a multi-mode silicon micro-ring modulator to mitigate its optical nonlinear effects by operating in the TE₁ mode. The TE₁ mode features a high nonlinear threshold compared with the TE₀ mode because of its larger waveguide loss and larger mode effective area. Under the condition of 10 mW optical input power, the resonance spectrum maintains a good symmetric Lorentz shape. The resonant wavelength shifts less than one resonance linewidth, showing an improved robustness to optical nonlinearity compared with regular silicon micro-ring modulators.

Rigorous dynamic model of a silicon ring resonator with phase change material for a neuromorphic node

The photonics platform has been considered increasingly promising for neuromorphic computing, due to its potential in providing low latency and energy efficient large-scale parallel connectivity. Phase change materials (PCMs) have been recently employed to introduce all-optical non-volatile memory in integrated photonic circuits, especially finding application as non-volatile weighting element in photonic artificial neural networks. Interestingly, these weighting elements can potentially be used as building blocks for large-scale networks that can autonomously adapt to their input, i.e. presenting the property of plasticity, similarly to the biological brain. In this work, we develop a computationally efficient dynamical model of a silicon ring resonator (RR) enhanced by a phase change material, namely Ge₂Sb₂Te₅ (GST). We do so starting from two existing dynamical models (of a silicon RR and of a GST thin film on a straight silicon waveguide), but extending the optical equations to properly account for the high absorption and asymmetry in the ring due to the phase change material. Our model accounts for silicon nonlinear effects due to free carriers and temperature, as well as for the phase change of GST, whose energy efficiency and optical contrast can be enhanced by the RR resonant behaviour. We also restructure the optical equations so that the model can be efficiently employed in a modular way within a commercial software for system-level photonics simulations. Moreover, exploiting the developed model, we explore several design parameters and show that both speed and energy efficiency of memory operations can be enhanced by factors from six to ten. Also, we show that the achievable optical contrast due to GST phase change can be increased by more than a factor ten by leveraging the resonant properties of the RR, at the expense of higher optical loss. Finally, by exploiting the nonlinear dynamics arising in silicon RR networks, we show that a strong contrast is achievable while preserving energy efficiency.

Study of nonlinear effects and self-heating in a silicon microring resonator including a Shockley-Read-Hall model for carrier recombination

A detailed description of the non-linear effects in silicon is needed when designing ring resonators in the silicon platform. The optical field propagating in the ring waveguide is strongly absorbed due to two-photon-absorption (TPA) and free-carrier-absorption (FCA), which become more prominent with increasing the input power in the ring. We present a new approach for the modelling of non-linear effects in silicon based ring resonators. We have numerically solved the non-linear problem coupling the variation of refractive index and loss due to TPA, FCA , self-heating and Shockley-Read-Hall (SRH) theory for trap-assisted recombination process. The model is validated by reproducing experimental measurements on a ring and a racetrack resonator having different Q-factors and waveguide cross-sections. As a result, we show that the SRH recombination is the origin of the dependence of free carrier lifetime on the power circulating in the ring and how this dependence is affected by the surface trap density and trap energy level. The model is then applied to the calculation of the maximum power that can incident the silicon rings designed for the Si PIC mirror of a hybrid III-V/Si widely tunable laser.

Photonic neuromorphic technologies in optical communications

Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.

Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations

Photonic implementations of reservoir computing (RC) promise to reach ultra-high bandwidth of operation with moderate training efforts. Several optoelectronic demonstrations reported state of the art performances for hard tasks as speech recognition, object classification and time series prediction. Scaling these systems in space and time faces challenges in control complexity, size and power demand, which can be relieved by integrated optical solutions. Silicon photonics can be the disruptive technology to achieve this goal. However, the experimental demonstrations have been so far focused on spatially distributed reservoirs, where the massive use of splitters/combiners and the interconnection loss limits the number of nodes. Here, we propose and validate an all optical RC scheme based on a silicon microring (MR) and time multiplexing. The input layer is encoded in the intensity of a pump beam, which is nonlinearly transferred to the free carrier concentration in the MR and imprinted on a secondary probe. We harness the free carrier dynamics to create a chain-like reservoir topology with 50 virtual nodes. We give proof of concept demonstrations of RC by solving two nontrivial tasks: the delayed XOR and the classification of Iris flowers. This forms the basic building block from which larger hybrid spatio-temporal reservoirs with thousands of nodes can be realized with a limited set of resources.