Optical Signal Processing Research Articles

Exploring the physical characteristics and nonlinear wave dynamics of a (3+1)-dimensional integrable evolution system

This study comprehensively explores the [Formula: see text]-dimensional Mikhailov–Novikov–Wang [Formula: see text] integrable equation, with the primary objective of elucidating its physical manifestations and establishing connections with analogous nonlinear evolution equations. The investigated model holds significant physical meaning across various disciplines within mathematical physics. Primarily, it serves as a fundamental model for understanding nonlinear wave propagation phenomena, offering insights into wave behaviors in complex media. Moreover, its relevance extends to nonlinear optics, where it governs the dynamics of optical pulses and solitons crucial for optical communication and signal processing technologies. Employing analytical methodologies, namely the unified [Formula: see text], Khater II ([Formula: see text]hat.II) method, and He’s variational iteration [Formula: see text] method, both numerical and analytical solutions are meticulously examined. Through this investigation, the intricate behaviors of the equation are systematically unveiled, shedding illuminating insights on various physical phenomena, notably including wave propagation in complex media and nonlinear optics. The outcomes not only underscore the efficacy of the analytical techniques deployed but also accentuate the equation’s pivotal role in modeling a broad spectrum of nonlinear wave dynamics. Consequently, this research significantly advances our comprehension of complex physical systems governed by nonlinear dynamics, thereby contributing notably to interdisciplinary pursuits in mathematical physics.

Deep learning-assisted structural health monitoring: acoustic emission analysis and domain adaptation with intelligent fiber optic signal processing

Abstract The Acoustic Emission (AE) technique is a well-established method for detecting and locating cracks as they occur. One of the key challenges in fracture mechanics is determining the cracking levels during crack propagation. By linking the AE signature with crack geometry, we can determine the stages of crack growth. The classification of cracking levels is established by correlating micro and macro crack damage to the stable and unstable propagation stages. In this paper, a deep learning architecture based on Convolutional Neural Network is proposed to automatically extract features from the AE signal and classify cracking levels. The performance of this Deep Learning (DL) approach is evaluated using a simulated and experimental Hsu-Nielsen Pencil-led break test conducted in Finite Element Modeling and laboratory setup via COMSOL and a novel High-finesse Fiber Bragg Gratings Fabry-Perot Interferometer sensing system respectively. The results demonstrate the effectiveness of the DL approach for cracking level identification with high accuracy. The dataset was artificially augmented, and a comparison was made between balanced and imbalanced datasets for training purposes. In addition to gaining a deeper understanding of the fracture process, a finite element model was created to differentiate between active and passive AE sources by simulating the Pencil-led break test and crack propagation under cyclic loading conditions respectively. The analysis of the simulation model reveals that the energy associated with AE hits, as calculated from fracture mechanics, increases. This is the primary distinction between active and passive AE sources.

MEMS Oscillators-Network-Based Ising Machine with Grouping Method.

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

Impact of pump-phase noise in PPLN-based optical parametric amplifier and wavelength converter for digital coherent transmission

Wideband signal amplification and optical signal processing with a high gain using an optical parametric amplifier based on a periodically poled LiNbO3 (PPLN) waveguide is attractive for constructing wideband optical fiber networks. We experimentally investigate the transfer characteristics of the phase noise of a pump laser in χ(2)-based optical parametric amplification and wavelength conversion on the basis of second-harmonic-generation and differential-frequency-generation processes. We also evaluate the effect of the transferred phase noise on signal quality in dispersion-unmanaged digital coherent fiber transmission systems. We show that the phase noise is transferred only to the wavelength-converted idler and does not affect the amplified signal even by using a pump laser with a MHz-order linewidth. We also show that the phase noise transferred to the idler light can have a similar impact on signal quality as equalization-enhanced phase noise (EEPN) in digital coherent transmission. The signal penalty including EEPN was evaluated with several pump lasers and at symbol rates of 32, 64, and 96 Gbaud. We also propose a method of using correlated pump lights between a wavelength converter pair to cancel out the transfer of phase noise.

Enhancing the performance of coherent Ising machines in the large-noise regime with a fifth-order nonlinearity

Enhancing the performance of coherent Ising machines in the large-noise regime with a fifth-order nonlinearity

Training an Ising machine with equilibrium propagation

Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate an efficient approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations. We employ the quantum annealing procedure of the D-Wave Ising machine to train a fully-connected neural network on the MNIST dataset. Furthermore, we demonstrate that the machine’s connectivity supports convolution operations, enabling the training of a compact convolutional network with minimal spins per neuron. Our findings establish Ising machines as a promising trainable hardware platform for AI, with the potential to enhance machine learning applications.

Multi‐Wavelength Optoelectronic Synaptic Transistors Based on Transition Metal Telluride‐Sulfide Heterostructures

AbstractNeuromorphic visual systems based on optogenetic techniques have colossal potential for in‐memory computing with prospects of developing artificial intelligence vision systems. However, conventional transistor architectures face formidable challenges in efficient signal processing owing to limitations in the intrinsic properties of active channel materials. In this work, a novel transition metal telluride‐sulfide hybrid heterojunction‐based optoelectronic synaptic phototransistor is proposed, in which UV–vis responsive zinc oxide encapsulated few‐layer tungsten disulfide channel is decorated with near‐infrared sensitive 0D cobalt ditelluride (CoTe2) nanocrystals (NCs), eliciting the ability to sense, store, and process optical signals across a broad range of the electromagnetic spectrum. This meticulously designed three‐layered heterostructure, based on their interfacial band alignments, enables high photoresponsivity up to ≈2.6 × 103 A W−1 at a back‐gate bias of 20 V, leading to the brain‐inspired synaptic applications with an average power consumption as low as 75 pJ for each training process. The device exhibits excitatory postsynaptic current, paired‐pulse facilitation with an index above 150%, as well as light‐modulated synaptic plasticity by mimicking biological synapses, which mainly originate from trapped holes in Co‐vacancy mediated surface defect states of CoTe2 NCs. Hence, this 2D material‐based hybrid phototransistor appears to be a promising candidate for energy‐efficient next‐generation brain‐inspired neuromorphic vision systems.

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets.

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. "Binary" stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and -1, with the probability of being in either level determined by an external bias. "Analog" stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence "analog") and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g., adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Dual-Criteria Decision Analysis by Multiphotonic Effects in Nanostructured ZnO

Simultaneous interrogation of pump and probe beams interacting in ZnO nanostructures of a two-wave mixing is proposed for dual-path data processing of optical signals by nonlinear optical effects. An enhancement in third-order nonlinear optical properties was exhibited by Al-doped ZnO thin films. Multiphoton absorption and nonlinear refraction were explored by the z-scan technique at 532 nm with nanosecond pulses. The evolution of the optical Kerr effect in the ZnO thin films was analyzed as a function of the incorporation of Al in the sample by a vectorial two-wave mixing method. Electrical and photoconductive effects were evaluated to further characterize the influence of Al in the ZnO solid samples. Potential applications of nonlinear optical parameters for encoding and encrypting information in light can be envisioned.

Partitioning QUBO with two-way one-hot conditions on traveling salesman problems for city distributions with multiple clusters

We introduce a method for solving a quadratic unconstrained binary optimization (QUBO) with the two-way one-hot constraints by dividing the QUBO into parts and solving it with an Ising machine. The one-hot constraint is a constraint condition in that only one binary variable takes the value one for a set of multiple binary variables. The two-way one-hot constraint imposes one-hot constraint conditions on every row and every column of a two-dimensional array of binary variables with equal numbers of rows and columns. For example, traveling salesman problems (TSPs) have two-way one-hot constraints. We propose two methods to solve a TSP by dividing the cities into clusters based on information in the QUBO matrix. The proposed methods decompose cities into clusters and solve TSPs for each cluster. We solve TSPs such that the cities are distributed in clusters and compare the results of the two proposed methods with the results of solving the problem without partitioning. The results show that the proposed methods are robust to the coefficient parameters in the Hamiltonian of QUBO and can obtain a solution closer to the optimal solution when solving the problem without partitioning is hard. We also discuss the application of the methods proposed in the TSPs to the quadratic assignment problems and to the problems of ordering things.

Investigation of Characteristics of Ultraviolet Light Pulse Weak Signal Communication System Based on Fourth-Order Frequency-Shift Keying Modulation

In ultraviolet (UV) communication, On–Off Keying (OOK) is the primary modulation technique. Compared to OOK, frequency modulation offers stronger resistance to path attenuation. Currently, research on frequency modulation demodulation schemes for UV communication is limited, mainly employing waveform detection and laser pulse response methods, which require high detection sensitivity to light. This study presents a novel frequency modulation communication scheme using discrete Poisson channel distribution and optical pulse signal processing algorithms, enhancing the signal processing sensitivity of the existing frequency modulation scheme to the level of photons. The proposed system model is rigorously evaluated through theoretical derivations and simulations. Additionally, a hardware system integrating optical pulse counting, frequency detection, and clock data recovery algorithms is developed. Experimental results show the system achieving a 5 kbps transmission rate under frequency modulation. In non-line-of-sight (NLOS) scenarios, communication reaches up to 65 m, with the receiver elevation angle ranging from 10° to 25° and the bit error rate (BER) stabilized at 10−4, while in line-of-sight (LOS) situations, the BER remains at 10−5 up to 400 m and 10−4 up to 700 m, achieving the farthest distance and fastest communication rate achievable in the current FSK modulation scheme of ultraviolet communication systems. The integrated components enhance its applicability in communication systems. This study offers a valuable addition to UV communication technology.

Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems

The growth of artificial intelligence leads to a computational burden in solving non-deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve NP-hard problems faces challenges such as high power consumption and limited scalability. Here, we experimentally present an Ising annealing computer based on 80 superparamagnetic tunnel junctions (SMTJs) with all-to-all connections, which solves a 70-city traveling salesman problem (TSP, 4761-node Ising problem). By taking advantage of the intrinsic randomness of SMTJs, implementing global annealing scheme, and using efficient algorithm, our SMTJ-based Ising annealer outperforms other Ising schemes in terms of power consumption and energy efficiency. Additionally, our approach provides a promising way to solve complex problems with limited hardware resources. Moreover, we propose a cross-bar array architecture for scalable integration using conventional magnetic random-access memories. Our results demonstrate that the SMTJ-based Ising computer with high energy efficiency, speed, and scalability is a strong candidate for future unconventional computing schemes.

Overdamped Ising machine with stochastic resonance phenomena in large noise condition

Overdamped Ising machine with stochastic resonance phenomena in large noise condition

A CMOS-compatible oscillation-based VO2 Ising machine solver

Phase-encoded oscillating neural networks offer compelling advantages over metal-oxide-semiconductor-based technology for tackling complex optimization problems, with promising potential for ultralow power consumption and exceptionally rapid computational performance. In this work, we investigate the ability of these networks to solve optimization problems belonging to the nondeterministic polynomial time complexity class using nanoscale vanadium-dioxide-based oscillators integrated onto a Silicon platform. Specifically, we demonstrate how the dynamic behavior of coupled vanadium dioxide devices can effectively solve combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems. The electrical mappings of these problems are derived from the equivalent Ising Hamiltonian formulation to design circuits with up to nine crossbar vanadium dioxide oscillators. Using sub-harmonic injection locking techniques, we binarize the solution space provided by the oscillators and demonstrate that graphs with high connection density (η > 0.4) converge more easily towards the optimal solution due to the small spectral radius of the problem’s equivalent adjacency matrix. Our findings indicate that these systems achieve stability within 25 oscillation cycles and exhibit power efficiency and potential for scaling that surpasses available commercial options and other technologies under study. These results pave the way for accelerated parallel computing enabled by large-scale networks of interconnected oscillators.

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.