Employing backpropagation, we introduce a supervised learning algorithm tailored for photonic spiking neural networks (SNNs). In supervised learning, algorithm information is represented by varying spike train strengths, and the SNN's training relies on diverse patterns involving varying spike counts among output neurons. The SNN utilizes a supervised learning algorithm for numerically and experimentally determining the classification. Within the SNN, photonic spiking neurons, built from vertical-cavity surface-emitting lasers, emulate the operational principles of leaky-integrate-and-fire neurons. The results showcase how the algorithm operates on the hardware. To optimize ultra-low power consumption and ultra-low delay, designing and implementing a hardware-friendly learning algorithm for photonic neural networks and achieving hardware-algorithm collaborative computing is essential.
For accurate measurement of weak periodic forces, a detector with a wide operational range and high sensitivity is crucial. Through a nonlinear dynamical locking mechanism of mechanical oscillation amplitude within optomechanical systems, we present a force sensor for detecting unknown periodic external forces, a detection method using the modified sidebands of the cavity field. The mechanical amplitude locking state allows an unknown external force to linearly adjust the locked oscillation's amplitude, hence establishing a linear proportionality between the sensor's sideband readings and the measured force's magnitude. The sensor's linear scaling range, found to be equivalent to the pump drive amplitude, permits measurement of a broad spectrum of force magnitudes. The sensor's efficacy at room temperature is attributable to the considerable robustness of the locked mechanical oscillation against thermal disturbances. This identical setup, beyond its ability to detect weak, periodic forces, can also identify static forces, albeit with a much narrower detection range.
The optical microcavities, known as plano-concave optical microresonators (PCMRs), are made up of a single planar mirror, a single concave mirror, and a spacer that divides them. PCMRs, illuminated by Gaussian laser beams, are crucial as sensors and filters in the scientific domains of quantum electrodynamics, temperature sensing, and photoacoustic imaging applications. The development of a model for Gaussian beam propagation through PCMRs, utilizing the ABCD matrix method, aimed to anticipate characteristics like the PCMR sensitivity. To confirm the model's predictions, interferometer transfer functions (ITFs) computed for a series of pulse code modulation rates (PCMRs) and beams were subjected to rigorous comparison with experimental measurements. A considerable accord was witnessed, signifying the model's soundness. Accordingly, it could be an effective instrument for designing and assessing PCMR systems in numerous professional spheres. The computer code enabling the model's function is publicly available online.
A generalized algorithm and mathematical model are presented for the multi-cavity self-mixing phenomenon, leveraging scattering theory. The utilization of scattering theory, a fundamental tool for studying traveling waves, reveals a recursive method for modeling self-mixing interference from multiple external cavities based on the individual characteristics of each cavity. A thorough examination reveals that the reflection coefficient of interconnected multiple cavities is contingent upon both the attenuation coefficient and the phase constant, thereby influencing the propagation constant. A significant advantage of recursively modeled systems is their computational efficiency when dealing with a large parameter space. Through the application of simulation and mathematical modeling, we demonstrate the tunability of individual cavity parameters, encompassing cavity length, attenuation coefficient, and refractive index of individual cavities, to yield a self-mixing signal with optimal visibility. The model under consideration intends to employ system descriptions for biomedical applications while exploring the behavior of multiple diffusive media with differing properties, but its scope can be expanded to any configuration.
The erratic actions of microdroplets during LN-based photovoltaic manipulation can induce transient instability and even failure in microfluidic handling. Laboratory biomarkers This paper systematically analyzes how water microdroplets respond to laser illumination on both uncoated and PTFE-coated LNFe surfaces, revealing that the abrupt repulsion of the microdroplets originates from an electrostatic shift from dielectrophoresis (DEP) to electrophoresis (EP). Water microdroplet charging, a consequence of Rayleigh jetting from an electrically charged water/oil interface, is proposed as the reason behind the DEP-EP transition. Analyzing the kinetic data of microdroplets against models for their photovoltaic-field motion reveals the charge accumulation on various substrate configurations (1710-11 and 3910-12 Coulombs on bare and PTFE-coated LNFe substrates), demonstrating the prevailing electrophoretic mechanism amidst the presence of both electrophoretic and dielectrophoretic forces. The practical realization of photovoltaic manipulation within LN-based optofluidic chips will depend critically on the outcomes derived from this study.
A flexible and transparent three-dimensional (3D) ordered hemispherical array polydimethylsiloxane (PDMS) film is presented in this paper to achieve both high sensitivity and uniform enhancement in surface-enhanced Raman scattering (SERS) substrates. A silicon substrate serves as the foundation for the self-assembled single-layer polystyrene (PS) microsphere array, achieving this. Regional military medical services The transfer of Ag nanoparticles onto the PDMS film, characterized by open nanocavity arrays formed by etching the PS microsphere array, is then accomplished through the liquid-liquid interface method. A soft, SERS-active sample, Ag@PDMS, is then prepared using an open nanocavity assistant. Employing Comsol's capabilities, we conducted an electromagnetic simulation of our sample. Measurements definitively show that the 50-nm silver particle-infused Ag@PDMS substrate excels in producing the strongest localized electromagnetic hot spots in the spatial domain. The Rhodamine 6 G (R6G) probe molecules encounter an exceptionally high sensitivity within the optimal Ag@PDMS sample, resulting in a limit of detection (LOD) of 10⁻¹⁵ mol/L and an enhancement factor (EF) of 10¹². Additionally, the substrate demonstrates a remarkably homogeneous signal intensity for probe molecules, with a relative standard deviation (RSD) of roughly 686%. Beyond that, it has the capability to detect multiple molecules simultaneously and to implement real-time detection techniques on surfaces that are not flat.
The electronically reconfigurable transmit array (ERTA) harmonizes the principles of optics and coding metasurfaces with the attributes of low-loss spatial feeding and the ability to manipulate beams in real time. A dual-band ERTA design presents a significant engineering challenge, due to the large mutual coupling effects accompanying dual-band operation and the requirement for separate phase control mechanisms in each band. We present a dual-band ERTA in this paper, enabling fully independent beam control in two divided frequency bands. Employing an interleaved arrangement within the aperture, the dual-band ERTA is built from two types of orthogonally polarized reconfigurable elements. The low coupling characteristic is established through the use of polarization isolation and a cavity that is connected to ground. A hierarchical bias approach is meticulously detailed to independently manage the 1-bit phase within each band. In order to ascertain the viability, a dual-band ERTA prototype was constructed, integrating 1515 upper-band components and 1616 lower-band components, followed by comprehensive measurement. Dabrafenib solubility dmso Within the 82-88 GHz and 111-114 GHz frequency bands, the experimental results demonstrate the successful implementation of independent beam manipulation utilizing orthogonal polarizations. Suitable for space-based synthetic aperture radar imaging, the proposed dual-band ERTA might prove to be a suitable choice.
A novel optical system for polarization image processing, utilizing geometric-phase (Pancharatnam-Berry) lenses, is presented in this work. In these lenses, acting as half-wave plates, the orientation of the fast (or slow) axis follows a quadratic relationship with the radial coordinate, leading to the same focal length for left and right circularly polarized light, but with opposite signs. Subsequently, they partitioned a collimated input beam into a converging beam and a diverging beam, bearing opposite circular polarizations. The coaxial polarization selectivity characteristic adds a novel degree of freedom to optical processing systems, making it compelling for imaging and filtering applications demanding polarization sensitivity. The presented properties allow us to develop an optical Fourier filter system that exhibits polarization sensitivity. Two Fourier transform planes, one for each circular polarization, are accessible through the use of a telescopic system. For the formation of a sole final image, a second symmetric optical system is instrumental in joining the two beams. Consequently, one can utilize polarization-sensitive optical Fourier filtering, as demonstrated through the application of simple bandpass filters.
For realizing neuromorphic computer hardware, analog optical functional elements, characterized by their high parallelism, rapid processing, and low power consumption, provide promising approaches. The utilization of convolutional neural networks in analog optical implementations is predicated on the Fourier transform characteristics observable in appropriately designed optical setups. Unfortunately, realizing the promise of optical nonlinearities within such neural networks for optimal performance presents significant hurdles to implementation. In this study, we detail the development and analysis of a three-layered optical convolutional neural network, where a 4f-imaging system forms the linear component, and optical nonlinearity is implemented using a cesium atomic vapor cell's absorption characteristics.