Optical Signal Processing Research Articles

Ultrafast laser inscription of 2D confinement MMI-based beam splitters with tunable splitting ratio in Nd:YAG crystal

The multimode-interference (MMI) devices based on the principle of self-imaging show great potential for applications in different scenarios. The research interest in two-dimensional confinement MMI devices has surged enormously due to the broader areas of applications compared with the one-dimensional confinement MMI devices. In this paper, we report on the characteristics of two-dimensional confinement 2 × 2 output MMI-based beam splitters which were achieved by femtosecond laser direct writing technology in a Nd:YAG crystal. The propagation features with different splitting ratios have been simulated by the beam propagation method. Two splitters with beam division performances of 2 × 2 output have been further demonstrated through an end-face coupling system and investigated with a laser beam at 532 nm wavelength, which have achieved two splitting ratios including 73 %: 12.5 %: 12.5 %: 2 % with a deviation less than 3 %, and 25 %: 25 %: 25 %: 25 % with a deviation less than 5 %. These performances with high consistency manifest that these devices can emerge into kinds of 3D optical applications with the ability of 3D spatial optical signal processing. Moreover, our work further proves that femtosecond laser direct writing technology presents absolute advantages in processing 3D micro-nano devices in transparent materials for on-chip integration.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

On-Chip Photonic Localization in Aharonov-Bohm Cages Composed of Microring Lattices.

Flatband localization endowed with robustness holds great promise for disorder-immune light transport, particularly in the advancement of optical communication and signal processing. However, effectively harnessing these principles for practical applications in nanophotonic devices remains a significant challenge. Herein, we delve into the investigation of on-chip photonic localization in AB cages composed of indirectly coupled microring lattices. By strategically vertically shifting the auxiliary rings, we successfully introduce a magnetic flux of π into the microring lattice, thereby facilitating versatile control over the localization and delocalization of light. Remarkably, the compact edge modes of this structure exhibit intriguing topological properties, rendering them strongly robust against disorders, regardless of the size of the system. Our findings open up new avenues for exploring the interaction between flatbands and topological photonics on integrated platforms.

Scalable almost-linear dynamical Ising machines

Scalable almost-linear dynamical Ising machines

Ultralow Power Electronic Analog of a Biological Fitzhugh-Nagumo Neuron.

Here, we introduce an electronic circuit that mimics the functionality of a biological spiking neuron following the Fitzhugh-Nagumo (FN) model. The circuit consists of a tunnel diode that exhibits negative differential resistance (NDR) and an active inductive element implemented by a single MOSFET. The FN neuron converts a DC voltage excitation into voltage spikes analogous to biological action potentials. We predict an energy cost of 2 aJ/cycle through detailed simulation and modeling for these FN neurons. Such an FN neuron is CMOS compatible and enables ultralow power oscillatory and spiking neural network hardware. We demonstrate that FN neurons can be used for oscillator-based computing in a coupled oscillator network to form an oscillator Ising machine (OIM) that can solve computationally hard NP-complete max-cut problems while showing robustness toward process variations.

General spatial photonic Ising machine based on the interaction matrix eigendecomposition method

The spatial photonic Ising machine has achieved remarkable advancements in solving combinatorial optimization problems. However, it still remains a huge challenge to flexibly map an arbitrary problem to the Ising model. In this paper, we propose a general spatial photonic Ising machine based on the interaction matrix eigendecomposition method. The arbitrary interaction matrix can be configured in the two-dimensional Fourier transformation based spatial photonic Ising model by using values generated by matrix eigendecomposition. The error in the structural representation of the Hamiltonian decreases substantially with the growing number of eigenvalues utilized to form the Ising machine. In combination with the optimization algorithm, as low as ∼65% of the eigenvalues are required by intensity modulation to guarantee the best probability of optimal solution for a 20-vertex graph Max-cut problem, and this percentage decreases to below ∼20% for near-zero probability. The 4-spin experiments and error analysis demonstrate the Hamiltonian linear mapping and ergodic optimization. Our work provides a viable approach for spatial photonic Ising machines to solve arbitrary combinatorial optimization problems with the help of the multi-dimensional optical property.

Parity-time symmetry enabled ultra-efficient nonlinear optical signal processing

Nonlinear optical signal processing (NOSP) has the potential to significantly improve the throughput, flexibility, and cost-efficiency of optical communication networks by exploiting the intrinsically ultrafast optical nonlinear wave mixing. It can support digital signal processing speeds of up to terabits per second, far exceeding the line rate of the electronic counterpart. In NOSP, high-intensity light fields are used to generate nonlinear optical responses, which can be used to process optical signals. Great efforts have been devoted to developing new materials and structures for NOSP. However, one of the challenges in implementing NOSP is the requirement of high-intensity light fields, which is difficult to generate and maintain. This has been a major roadblock to realize practical NOSP systems for high-speed, high-capacity optical communications. Here, we propose using a parity-time (PT) symmetric microresonator system to significantly enhance the light intensity and support high-speed operation by relieving the bandwidth-efficiency limit imposed on conventional single resonator systems. The design concept is the co-existence of a PT symmetry broken regime for a narrow-linewidth pump wave and near-exceptional point operation for broadband signal and idler waves. This enables us to achieve a new NOSP system with two orders of magnitude improvement in efficiency compared to a single resonator. With a highly nonlinear AlGaAs-on-Insulator platform, we demonstrate an NOSP at a data rate approaching 40 gigabits per second with a record low pump power of one milliwatt. These findings pave the way for the development of fully chip-scale NOSP devices with pump light sources integrated together, potentially leading to a wide range of applications in optical communication networks and classical or quantum computation. The combination of PT symmetry and NOSP may also open up opportunities for amplification, detection, and sensing, where response speed and efficiency are equally important.

Parametric Frequency Divider Based Ising Machines.

We report on a new class of Ising machines (IMs) that rely on coupled parametric frequency dividers (PFDs) as macroscopic artificial spins. Unlike the IM counterparts based on subharmonic-injection locking (SHIL), PFD IMs do not require strong injected continuous-wave signals or applied dc voltages. Therefore, they show a significantly lower power consumption per spin compared to SHIL-based IMs, making it feasible to accurately solve large-scale combinatorial optimization problems that are hard or even impossible to solve by using the current von Neumann computing architectures. Furthermore, using high quality factor resonators in the PFD design makes PFD IMs able to exhibit a nanowatt-level power per spin. Also, it remarkably allows a speedup of the phase synchronization among the PFDs, resulting in shorter time to solution and lower energy to solution despite the resonators' longer relaxation time. As a proof of concept, a 4-node PFD IM has been demonstrated. This IM correctly solves a set of Max-Cut problems while consuming just 600 nanowatts per spin. This power consumption is 2 orders of magnitude lower than the power per spin of state-of-the-art SHIL-based IMs operating at the same frequency.

Surface Acoustic Microwave Photonic Filters on Etchless Lithium Niobate Integrated Platform

AbstractLithium niobate on insulator emerges as a promising platform for integrated microwave photonics because of its capability of ultralow‐loss guidance and high‐efficiency modulation of light. Chip‐level integration of microwave filters is important for signal processing in the 5G/6G wireless communication. Here, by employing bound states in the continuum for low‐loss waveguiding, high‐performance microwave photonic filters are realized on an etchless lithium niobate integrated platform. These microwave photonic filters consist of a high‐quality photonic microcavity modulated by piezoelectrically excited surface acoustic waves. Acoustic time delays from 21 to 106 ns and passbands with bandwidth as narrow as 0.89 MHz are achieved in the fabricated filters operating at gigahertz frequencies. This demonstration may open up new applications on the lithium niobate integrated platform, such as optical communication, signal processing, and beam steering.

An Integrated All‐Optical Ising Machine with Unlimited Spin Array Size and Coupling

Artificial spin systems are used to solve combinatorial optimization problems by mapping them to the ground state search of the Ising model. Ising machines of various designs, in fields as diverse as photonics and electronics, have been proposed and demonstrated in recent years. One important mathematical operation required in photonic Ising machines for nearly all Hamiltonian minimization algorithms is repeated matrix–vector multiplication with the vector size limited by the physical size of the optical system. Herein, an integrated photonic Ising machine based on matrix partitioning and phase encoding, which effectively allows the use of physically small optical circuits to handle arbitrarily large spin vectors, is proposed. All the complicated calculations are carried out optically, rather than electronically, in this approach. How the required surface area of a hypothetical chip‐based photonic Ising machine can be resized according to convenience, with the trade‐off being the solution time, and that this trade‐off can be done in a way that does not impact the algorithm's efficacy, is shown. Through simulation, the system is predicted to have a high success rate, high noise tolerance, and high error tolerance.

Optical Darboux Transformer.

The optical Darboux transformer for solitons is introduced as a photonic device that performs the Darboux transformation directly in the optical domain. This enables two major advances for optical signal processing based on the nonlinear Fourier transform: (i)the multiplexing of solitonic waveforms corresponding to different discrete eigenvalues of the Zakharov-Shabat system, and (ii)the selective filtering of an arbitrary number of individual solitons too. The optical Darboux transformer can be built using existing commercially available photonic technology components and constitutes a universal tool for signal processing, optical communications, optical rogue waves generation, and waveform shaping and control in the nonlinear Fourier domain.

Topologically Protected Quantum Logic Gates with Valley-Hall Photonic Crystals.

Topological photonics provide a promising way to realize more robust optical devices against some defects and environmental perturbations. Quantum logic gates are fundamental units of quantum computers, which are widely used in future quantum information processing. Thus, constructing robust universal quantum logic gates is an important way forward to practical quantum computing. However, the most important problem to be solved is how to construct the quantum-logic-gate-required 2×2 beam splitter with topological protection. Here, the experimental realization of the topologically protected contradirectional coupler is reported, which can be employed to realize the quantum logic gates, including control-NOT and Hadamard gates, on the silicon photonic platform. These quantum gates not only have high experimental fidelities but also exhibit a certain degree of tolerances against certain types of defects. This work paves the way for the development of practical optical quantum computations and signal processing.

Ferroelectric compute-in-memory annealer for combinatorial optimization problems

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

A general learning scheme for classical and quantum Ising machines

An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained using gradient descent. We provide a mathematical characterization of the training process, which is based upon optimizing a loss function whose partial derivatives are not explicitly calculated but estimated by the Ising machine itself. Moreover, we present some experimental results on the training and execution of the proposed learning model. These results point out new possibilities offered by Ising machines for different learning tasks. In particular, in the quantum realm, the quantum resources are used for both the execution and the training of the model, providing a promising perspective in quantum machine learning.

Polychromatic photonic Floquet-Bloch oscillations.

Photonic Floquet-Bloch oscillations (FBOs), a new type of Bloch-like oscillations in photonic Floquet lattices, have recently been observed as a typical discrete self-imaging effect. Here, we theoretically investigate the spectral range of approximate photonic Floquet-Bloch oscillations in arrays of evanescently coupled optical waveguides and show the adjustability of the spectral range. At an appropriate amplitude of the Floquet modulation, we have demonstrated approximate photonic FBOs over a broad spectral range, termed "polychromatic photonic Floquet-Bloch oscillations," which manifest as approximate self-imaging of polychromatic beams. Furthermore, by designing the functional form of the Floquet modulation, we can cascade two polychromatic photonic FBOs and further enhance the performance of polychromatic self-imaging. Our results provide a simple and novel mechanism for achieving polychromatic self-imaging in waveguide arrays and may find applications in polychromatic beam shaping and broadband optical signal processing.

Parametric excitations of coupled nanomagnets

We demonstrate that parametrically excited eigenmodes in nearby nanomagnets can be coupled to each other. Both positive (in-phase) and negative (anti-phase) couplings can be realized by a combination of appropriately chosen geometry and excitation field frequency. The oscillations are sufficiently stable against thermal fluctuations. The phase relation between field-coupled nanomagnets shows a hysteretic behavior with the phase relation being locked over a wide frequency range. We envision that this computational study lays the groundwork to use field-coupled nanomagnets parametrons as building blocks of logic devices, neuromorphic systems of Ising machines.

Linear Terahertz Frequency Conversion in a Temporal‐Boundary Metasurface

AbstractLinear frequency conversion enabled by a temporal boundary of a resonant cavity provides a promising alternative to generating new frequency components, where the temporal boundary can be introduced by rapidly modifying the refractive index of the cavity. However, the perturbed photon dynamics during the ultrafast linear process is still vague. Here, the photon dynamics based on temporal coupled‐mode theory are explored and the phenomena are demonstrated with a silicon metasurface via simulations and experiments in the terahertz regime. With the time‐varying real and imaginary parts of the refractive index, a periodic modulation (Td) of the amplification coefficient of the newly generated photons is observed where the optimized coefficient occurs in the first period at td = Td /2. It further establishes a dynamic critical coupling condition to improve the amplification coefficient in the dynamic process. The scheme of linear frequency conversion via temporal boundary can revolutionize the paradigm of parametric amplification from nonlinear to linear processes, and conversion efficiency will be improved in a high‐quality factor cavity. The ultrafast modulation will find applications in optical signal processing, modulators, and reconfigurable intelligent surfaces for development of the next‐generation communications.

Coherent control of enhanced second-harmonic generation in a plasmonic nanocircuit using a transition metal dichalcogenide monolayer

Nonlinear nanophotonic circuits, renowned for their compact form and integration capabilities, hold potential for advancing high-capacity optical signal processing. However, limited practicality arises from low nonlinear conversion efficiency. Transition metal dichalcogenides (TMDs) could present a promising avenue to address this challenge, given their superior optical nonlinear characteristics and compatibility with diverse device platforms. Nevertheless, this potential remains largely unexplored, with current endeavors predominantly focusing on the demonstration of TMDs’ coherent nonlinear signals via free-space excitation and collection. In this work, we perform direct integration of TMDs onto a plasmonic nanocircuitry. By controlling the polarization angle of the input laser, we show selective routing of second-harmonic generation (SHG) signals from a MoSe2 monolayer within the plasmonic circuit. Routing extinction ratios of 14.86 dB are achieved, demonstrating good coherence preservation in this hybrid nanocircuit. Additionally, our characterization indicates that the integration of TMDs leads to a 13.8-fold SHG enhancement, compared with the pristine nonlinear plasmonic nanocircuitry. These distinct features—efficient SHG generation, coupling, and controllable routing—suggest that our hybrid TMD-plasmonic nanocircuitry could find immediate applications including on-chip optical frequency conversion, selective routing, switching, logic operations, as well as quantum operations.

Configurable SNAP microresonators induced by axial pre-strain-assisted CO2 laser exposure.

Flexible engineering of the complex shapes of the surface nanoscale axial photonics (SNAP) bottle microresonators (SBMs) is challenging for future nanophotonic technology applications. Here, we experimentally propose a powerful approach for the one-step fabrication of SBMs with simultaneous negative and positive radius variations, exhibiting a distinctive "bump-well-bump" profile. It is executed by utilizing two focused and symmetrical CO2 laser beams exposed on the fiber surface for only several hundred milliseconds. The spectral characteristics of different eigenmodes are analyzed, providing deep insights into the complex physical processes during the CO2 laser exposure. The shapes of the SBMs can be flexibly adjusted by the exposure time, laser power, and applied pre-strains. As a proof of this technique, the developed approach enables the efficient production of a bat SBM, ensuring a uniform field amplitude of the bat mode over the length exceeding 120 µm with 7% deviation. Our proposed technique provides a powerful technique for the efficient fabrication of SBMs with predetermined shapes, laying the groundwork for its applications on microscale optical signal processing, quantum computing, and so on.

Reconfigurable integrated photonic filters for optical signal processing using a silicon photonic platform.

We present a systematic photonic filter design approach by deploying pole-zero optimization. The filter transfer function is derived from its specifications by formulating closed-form optimization objective functions and subsequently translating them into optical design parameters. Two distinct filter examples, namely Chebyshev and elliptic filters, are considered for the design and validation. A compact reconfigurable three-pole photonic filter is fabricated on a silicon photonic platform to illustrate the proposed design technique including transmission tunability. Integrated thermal phase shifters coupled with micro-ring resonators are used to reconfigure filter responses. A well-matched experimental demonstration is presented to validate the proposed tuning method. We achieved a sharp out-of-band edge rejection of at least 20 and 40 dB for the elliptic and Chebyshev filter, respectively.