We present a supervised learning algorithm for photonic spiking neural networks (SNNs), leveraging backpropagation. 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. Numerically and experimentally, the classification task within the SNN is undertaken using a supervised learning algorithm. Vertical-cavity surface-emitting lasers underpin the photonic spiking neurons that form the SNN, exhibiting operational characteristics analogous to those of leaky-integrate-and-fire neurons. The demonstration of the algorithm's implementation on the hardware is verified by the results. A crucial step towards ultra-low power consumption and ultra-low delay in photonic neural networks involves designing and implementing a hardware-friendly learning algorithm, alongside hardware-algorithm collaborative computing.
The measurement of weak periodic forces demands a detector characterized by both a broad operating range and high sensitivity. We propose a force sensor, grounded in a nonlinear dynamical mechanism for locking mechanical oscillation amplitude within optomechanical systems, which detects unknown periodic external forces by analyzing the resulting modifications to the cavity field's sidebands. 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 capacity to measure a broad spectrum of force magnitudes is due to the linear scaling range, which corresponds to the amplitude of the applied pump drive. The sensor's efficacy at room temperature is attributable to the considerable robustness of the locked mechanical oscillation against thermal disturbances. Static forces, in addition to weak, cyclical forces, are detectable using the same configuration, although the scope of detection is markedly diminished.
Optical microcavities, called plano-concave optical microresonators (PCMRs), are fashioned from one planar mirror and one concave mirror, separated by a spacer element. In the fields of quantum electrodynamics, temperature sensing, and photoacoustic imaging, PCMRs are utilized as sensors and filters, illuminated by Gaussian laser beams. Based on the ABCD matrix method, a Gaussian beam propagation model through PCMRs was established to predict characteristics such as the PCMR's 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. The observed agreement validates the model's efficacy. It could, accordingly, prove to be a helpful tool in the design and evaluation of PCMR systems within various sectors. For public access, the computer code which powers the model has been made available online.
A generalized algorithm and mathematical model are presented for the multi-cavity self-mixing phenomenon, leveraging scattering theory. In the study of traveling waves, scattering theory is extensively employed to demonstrate that self-mixing interference from multiple external cavities can be recursively modeled by individually characterizing each cavity's parameters. The comprehensive investigation highlights that the equivalent reflection coefficient of coupled multiple cavities is dependent upon both the attenuation coefficient and the phase constant, and, hence, the propagation constant. The computational efficiency of recursive models is noteworthy when tackling the modeling of a significant number of parameters. We demonstrate, using simulation and mathematical modeling, the manner in which the individual cavity parameters, including cavity length, attenuation coefficient, and refractive index of each cavity, are tuned to achieve 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.
Transient instability and possible failure in microfluidic operations may arise from the unpredictable behavior of microdroplets subjected to LN-based photovoltaic manipulation. selleck chemicals llc This study systematically examines the response of water microdroplets to laser illumination on LNFe surfaces, both bare and PTFE-coated, and finds that the abrupt repulsion observed is a consequence of a change from dielectrophoresis (DEP) to electrophoresis (EP) in the electrostatic mechanism. The Rayleigh jetting, originating from an electrified water/oil interface, is proposed as the mechanism responsible for the DEP-EP transition, specifically the charging of water microdroplets. Microdroplet kinetic data, when matched against models portraying photovoltaic-field-influenced movement, uncovers the charging magnitude on substrate variations (1710-11 and 3910-12 Coulombs on bare and PTFE-coated LNFe substrates, respectively), affirming the electrophoretic mechanism's superiority in the presence of both dielectrophoretic and electrophoretic mechanisms. The practical realization of photovoltaic manipulation within LN-based optofluidic chips will depend critically on the outcomes derived from this study.
This paper proposes the preparation of a flexible and transparent three-dimensional (3D) ordered hemispherical array polydimethylsiloxane (PDMS) film for the dual purpose of achieving high sensitivity and uniformity in surface-enhanced Raman scattering (SERS) substrates. A single-layer polystyrene (PS) microsphere array is self-assembled onto a silicon substrate to accomplish this. Osteogenic biomimetic porous scaffolds 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. Finally, an open nanocavity assistant is utilized to prepare the Ag@PDMS soft SERS sample. Comsol software was employed for the electromagnetic simulation of our sample. Empirical evidence confirms that the Ag@PDMS substrate, incorporating 50-nanometer silver particles, is capable of concentrating electromagnetic fields into the strongest localized hot spots in the spatial region. The exceptionally sensitive Ag@PDMS sample demonstrates a limit of detection (LOD) of 10⁻¹⁵ mol/L and an enhancement factor (EF) of 10¹² for Rhodamine 6 G (R6G) probe molecules. The substrate's signal intensity for probe molecules is exceptionally uniform, resulting in a relative standard deviation (RSD) of approximately 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 core functionality of electronically reconfigurable transmit arrays (ERTAs) lies in the real-time beam manipulation enabled by their unique blend of optical theory, coding metasurface mechanism, and low-loss spatial feeding. Developing a dual-band ERTA presents a formidable challenge, stemming from the significant mutual coupling effects inherent in dual-band operation and the need for separate phase control in each frequency band. We demonstrate in this paper a dual-band ERTA system enabling fully independent beam manipulation in two discrete frequency bands. Within the aperture, two orthogonally polarized reconfigurable elements, arranged in an interleaved structure, create the dual-band ERTA. Low coupling is realized through the strategic application of polarization isolation and a cavity connected to the ground. A meticulously designed hierarchical bias method is introduced for the independent control of the 1-bit phase in each band. To demonstrate the feasibility, a dual-band ERTA prototype, comprising 1515 upper-band elements and 1616 lower-band elements, was meticulously designed, constructed, and evaluated. migraine medication The results of the experiments show successful independent beam control with orthogonal polarization techniques within the 82-88 GHz and 111-114 GHz frequency bands. Space-based synthetic aperture radar imaging could find the proposed dual-band ERTA to be a fitting candidate.
Employing geometric-phase (Pancharatnam-Berry) lenses, this work introduces a novel optical system for processing polarization images. These half-wave plates, which are lenses, have a fast (or slow) axis orientation that changes quadratically with the radial distance, resulting in the same focal length for left and right circular polarizations, but with differing signs. Therefore, the parallel input beam was divided into a converging beam and a diverging beam, each with mutually opposed circular polarization. Optical processing systems, through coaxial polarization selectivity, gain a new degree of freedom, which makes it very appealing for applications such as imaging and filtering, particularly those which require polarization sensitivity. By capitalizing on these inherent properties, we create an optical Fourier filtering system that is sensitive to polarization. The telescopic system is designed to provide access to two Fourier transform planes, one for each circular polarization. The two beams are recombined into a single final image by the application of a second symmetrical optical system. Consequently, polarization-sensitive optical Fourier filtering proves applicable, as exemplified by straightforward bandpass filters.
Fast processing speeds, low power consumption, and a high degree of parallelism in analog optical functional elements make them compelling candidates for constructing neuromorphic computer hardware. Analog optical implementations are facilitated by convolutional neural networks, leveraging the Fourier transform properties of strategically designed optical systems. There remain considerable obstacles in effectively employing optical nonlinearities for these particular neural networks. 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.