This article presents a two-stage, biologically inspired neuromorphic framework for autonomous robot navigation in cluttered environments. The proposed architecture combines unsupervised spiking neural network (SNN)-based sensory abstraction with reward-modulated spike-timing-dependent plasticity (R-STDP) for decision-making. In the first stage, raw 360${}<^>{\circ }$ LiDAR measurements are transformed into a compact, interpretable obstacle state through a lateral-inhibition-driven STDP network, yielding a low-dimensional, behaviorally relevant perception of the environment. In the second stage, navigation actions are learned via reward-modulated STDP operating on this abstracted state, supporting long-horizon goal-directed behavior without backpropagation or deep reinforcement learning. Two navigation paradigms are investigated: conventional goal-oriented navigation using relative bearing information, and a probabilistic field-based formulation that enables source-seeking behavior under goal uncertainty. The proposed approach is evaluated extensively in Gazebo and NVIDIA Isaac Sim using a TurtleBot3 platform across static, dynamic, and near-realistic environments. Experimental results demonstrate reliable navigation performance, competitive success and collision rates compared to state-of-the-art SNN and hybrid SNN-RL methods, and substantially lower estimated energy consumption. These findings highlight the effectiveness of modular, biologically plausible neuromorphic architectures for energy-efficient autonomous navigation in complex environments.

We report mode-resolved mechanical evidence that visible-light illumination reversibly modifies the coupled conservative and dissipative response of a nanoscale tip-aqueous junction. Using the passive multimode resonance spectrum of an undriven atomic force microscope cantilever as a local mechanical readout, we observe reversible, near-field-localized changes in resonance frequency and line width upon 532 nm illumination of a deliquesced CaCl2 droplet. Distance-dependent measurements, together with dry-glass and silicone-oil controls, confirm the signal is localized to the aqueous interface's near-field. Analysis of the two lowest eigenmodes rules out purely mass-loaded or conservative origins. Only a model considering both conservative and dissipative contributions reproduces all four modal observables. The extracted conservative perturbation matches a nanoscale capillary force-gradient estimate. The relaxation time scale is consistent with interfacial meniscus kinetics rather than local thermal diffusion. These results establish a nanoscale mechanical framework for probing illuminated aqueous interfaces, constraining the magnitude, dissipative character, and time scale for any proposed mechanism.

Electromagnetic (EM) metamaterials have seen significant advances in their design and application, especially for radar cross section (RCS) reduction and stealth applications. The growing complexity of design methods, the wide variety of techniques explored, and the broad range of applications make it necessary to consolidate this knowledge into a comprehensive survey. Despite the wealth of research in this domain, no unified review covers theoretical foundations and practical guidelines. This review offers a detailed synthesis of the most important advances, providing a clear and structured understanding of how different techniques, such as beam diffusion, field cancelation, polarization conversion, and wave absorption, are exploited within the broader context of EM metamaterials for RCS reduction. In addition, expressions are provided for the evaluation of the reduction in RCS, guidelines are offered for the design of innovative metamaterials, and novel application areas beyond traditional defense applications are explored, highlighting the potential of these technologies in commercial and civilian domains. Moreover, this work compiles and evaluates the key milestones and contributions that have shaped the field, presenting them in a manner that allows for the rapid identification of critical developments and trends. By identifying the challenges still faced in the area of RCS reduction and providing insights into future research directions, this review serves as both a resource and a roadmap for future innovations in the field.

Traditional concentrator photovoltaics (CPV) systems cannot capture diffuse light and require bulky suntracking. In this work, we characterize a commercially oriented, semi-transmissive, micro-CPV with integrated tracking optically, thermally, and electrically under the sand-laden, hot desert climate of Abu Dhabi, United Arab Emirates to evaluate its potential for agrivoltaics and ability to contribute towards the interplay of the foodenergy nexus. When the module operates in an "Electricity" mode (E-mode) by focusing direct light on microcells, the peak electrical efficiency was 26.9 % while 14-26 % of the incoming irradiance was uniformly transmitted as diffuse light underneath. When climate conditions are not favourable for electric generation, switching to a "Maximum Light Transmission" mode (MLT-mode) yielded a transmittance up to 71.61 %, providing a daily illuminance (daylighting applications) of 1.84 x 109 lumens m-2 day-1 and Daily Light Integral (DLI) dose of 29.69 molPAR m-2 day-1, suitable for moderate-to-high light crops. On the thermal front, the air temperature beneath the panel surpasses the ambient temperature by a range of 10-15 degrees C, and the solar cell temperatures ran similar to 27 degrees C above ambient at midday. Based on the data and observations of a 1-year crop-agnostic module characterization, we identify design improvement and manufacturing errors, mostly in the field of optics and tracking-integration.

Metal oxide nanostructures have recently gained high attention due to advances in their synthesis, particularly hydrothermal techniques, which allow precise control over their morphology, composition, and crystallinity, as well as integration into devices. Zinc-tin oxide (ZTO) nanostructures, in particular, are notable for their sustainability and multifunctional applications, including catalysis, electronics, sensors, and energy harvesting. Their ternary oxide nature supports a broad range of functionalities. The use of seed layers during synthesis has proven to be beneficial, particularly for binary systems such as ZnO, as it not only impacts the growth of nanostructures but is also advantageous for applications requiring nanostructures supported on substrates, such as in photocatalysis and sensor technologies. This work investigates the effect of various seed layers (e.g., Cu, stainless steel, Cr, Ni) on the hydrothermal synthesis of ZTO nanostructures. Compared to seed layer free methods under similar conditions, the presence of seed layers significantly influenced the resulting structures. The study produced diverse morphologies, including ZnSnO₃ nanowires and Zn₂SnO₄ nanoparticles, octahedrons, and nanowires. Findings suggest a relationship between the seed layer’s phase and the resulting nanostructure phase. Furthermore, shorter synthesis durations favored discrete nanostructures, while longer durations facilitated the formation of thin films with nanostructured surfaces. These observations underscore the dual role of seed layers in influencing both the structural phase and growth kinetics of ZTO nanostructures.

Designing multilayer thin-film structures with tailored optical responses is a complex and computationally intensive task, often requiring repeated simulations and limited physical intuition about the role of each layer. In this study, we propose a data-driven framework that leverages a fully connected neural network (FCNN) as a surrogate model to accelerate and interpret the design of a high-reflectivity multilayer structure composed of alternating Ta2O5 and MgF2 layers. The FCNN model is trained to predict reflectance spectra with high accuracy over a wide design space defined by the thicknesses of individual layers. Beyond fast prediction, we analyze the physical influence of each design parameter using Principal Component Analysis (PCA), Global Sensitivity Analysis, and SHapley Additive exPlanations (SHAP). These techniques provide deep insights into layer significance, symmetry, and redundancy in the design, shedding light on the complex relationships between structure and spectral response. Finally, we integrate the surrogate model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm to maximize both the transmission peak and its full width at half maximum (FWHM) for a desired wavelength band.We then elect the best solution using a Multi-Criteria Decision-Making (MCDM) approach. This work demonstrates the synergy between AI and photonic design, enabling efficient optimization and facilitating physical interpretability. The framework provides a robust pathway for the intelligent design of optical filters and mirrors, with potential applications across telecommunications, sensing, and laser systems.

Spectral imaging systems enable a significant leap forward in identifying and characterizing materials and objects through their unique spectral signatures. This review explores the advances in spectral imaging systems (SIS) designs, analyzing both the techniques and technologies. It provides an overview of the fundamental principles of spectral imaging, including different panchromatic, multispectral, and hyperspectral imaging systems. Progress in the design of multispectral imaging (MSI) systems are comprehensively surveyed with focus on the optical technologies employed. Advances in hyperspectral imaging (HSI) system are discussed, covering various acquisition techniques such as staring, snapshot, whiskbroom, and pushbroom techniques. In the case of staring technique, an in-depth examination is provided on different filtering technologies, including dispersive and diffractive optics, acousto-optic tunable filters (AOTFs), Fabry-Perot interferometers (FPI), liquid crystal tunable filters (LCTFs), and linear variable filters (LVFs). A detailed overview and progress in snapshot techniques is provided, followed by a thorough examination of whiskbroom and pushbroom techniques. Finally, it offers insights into the potential future directions in SIS research.

Reflective metalenses with multilayer substrates offer low-loss wavefront control in the LWIR range but remain underexplored and highly dependent on material choice. Here, we report a comprehensive numerical investigation into the design of LWIR reflective metalenses employing dielectric distributed Bragg reflectors (DBRs) substrates composed of high-index semiconductors (Si, Ge, GaAs) and zinc-based dielectric compounds (ZnO, ZnSe, ZnS). We systematically evaluate nine DBR material combinations to assess their impact on the focusing efficiency, reflectivity, and focal spot characteristics. The designed metalens, with an aperture diameter of [Formula: see text] [Formula: see text] and a focal length of 0.7 [Formula: see text], operate at a design wavelength of [Formula: see text] [Formula: see text]. All configurations achieve high reflectance [Formula: see text] over a broad spectral range, with Si/ZnSe and GaAs/ZnO based designs exhibiting the highest focusing efficiencies of [Formula: see text] and [Formula: see text] respectively, at Numerical Aperture (NA) [Formula: see text]. All the examined configurations provide nearly complete [Formula: see text] phase coverage, yielding diffraction-limited focal spot sizes ranging from[Formula: see text] to [Formula: see text]. We further analyze the impact of NA and metasurface unit cell periodicity ([Formula: see text]) on the lens performance, demonstrating that smaller unit cell periods improve phase discretization and optical response uniformity, while increasing NA results in tighter focal spots, with diminishing improvements near the diffraction limit.

In this work, we present a bio-inspired approach for home localization using event-based visual data and spiking convolutional neural networks (S-CNNs) in a simulated environment within NVIDIA Omniverse. Drawing inspiration from the navigational strategies of insects, which utilize visual cues and homing vectors to return to their nests, our approach leverages event cameras that mimic the asynchronous change-driven visual processing of insect eyes. To process this sparse, event-driven input, we employ spiking neural networks (SNNs), which mirror the spike-based transmission of information in biological neural systems. The proposed system utilizes a quadrotor equipped with an event camera to capture dynamic, asynchronous visual data and processes it using an S-CNN trained to estimate relative home vectors. This vector encoding method is inspired by the unit-circle representation of gaze directions observed in insect homing behavior, as outlined in recent studies. By integrating event-based vision and SNNs, our approach ensures energy-efficient computation, robustness to lighting variations, and adaptability to dynamic scenes. We validate the framework in a 3D environment modeled within ISAAC Sim, where the quadrotor autonomously navigates back to a designated "nest" location. Comparative analysis with conventional frame-based neural networks demonstrates the superiority of the proposed system in terms of efficiency, robustness, and accuracy. This work establishes a novel bio-inspired framework for integrating event-based data and spiking neural networks, paving the way for energy-efficient localization in robotics and smart home environments. Future work will explore the deployment of the system in a multi-quadrotor environment to coordinate collaborative tasks including the integration of multi-modal sensory inputs such as Camera, IMU, Gas sensors and GPS data, and the extension of the framework to real-world settings for further validation and scalability.

We design, simulate, and experimentally demonstrate a coarse wavelength-division de-multiplexing system based on cascaded Mach-Zehnder interferometers (MZIs) operating in the O-band. The fabricated devices are built on a monolithic silicon photonics platform in a state-of-the-art CMOS foundry. The fabrication tolerance of the device was obtained by a combination of analytical and 3D Finite-difference time-domain (FDTD) methods. Fabrication-tolerant wavelength-independent couplers were used in order to achieve broadband splitting ratios (SRs) and ensure device stability along the entire wavelength range. In addition, phase-balanced linear tapers were used to allow for arbitrary waveguide width values for the MZIs. Experimental results show very high-performance stability across different wafer test sites with a mean channel spectral shift of only 1.03 nm and a total device footprint of 0.582 mm $^{2}$ .

Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes.

Grating-assisted contra-directional couplers (CDCs) wavelength selective filters for wavelength division multiplexing (WDM) are designed and experimentally demonstrated. Two configuration setups are designed; a straight-distributed Bragg reflector (SDBR) and curved distributed Bragg reflector (CDBR). The devices are fabricated on a monolithic silicon photonics platform in a GlobalFoundries CMOS foundry. The sidelobe strength of the transmission spectrum is suppressed by controlling the energy exchange between the asymmetric waveguides of the CDC using grating and spacing apodization. The experimental characterization demonstrates a flat-top and low insertion loss (0.43 dB) spectrally stable performance (<0.7 nm spectral shift) across several different wafers. The devices have a compact footprint of only 130µm2/Ch (SDBR) and 3700µm2/Ch (CDBR).

We experimentally demonstrate wavelength-independent couplers (WICs) based on an asymmetric Mach-Zehnder interferometer (MZI) on a monolithic silicon-photonics platform in a commercial, 300-mm, CMOS foundry. We compare the performance of splitters based on MZIs consisting of circular and 3rd order (cubic) Bézier bends. A semi-analytical model is constructed in order to accurately calculate each device's response based on their specific geometry. The model is successfully tested via 3D-FDTD simulations and experimental characterization. The obtained experimental results demonstrate uniform performance across different wafer sites for various target splitting ratios. We also confirm the superior performance of the Bézier bend-based structure, compared to the circular bend-based structure both in terms of insertion loss (0.14 dB), and performance consistency throughout different wafer dies. The maximum deviation of the optimal device's splitting ratio is 0.6%, over a wavelength span of 100 nm. Moreover, the devices have a compact footprint of 36.3 × 3.8 μ m 2.

We studied a Mach–Zehnder Interferometer (MZI)-based electrolytic sensor on a silicon-on-insulator (SOI) platform. First, the Si waveguide, integrated with the dielectric layer (SiO2) and operating under varying pH, is designed using commercial software Nextnano. The impact of the band bending in the SiO2 integrated with the Si waveguide is presented. Parameters obtained using Nextnano from the designed Si waveguide structure, under varying pH values of electrolytic solutions, were adopted to design an MZI-based electrolytic sensor. We also present a simple strategy for experimental verification of liquid electrolyte’s effects on the optical properties of SiO2/p-Si using spectroscopic ellipsometry. Two different schemes of MZI-based electrolytic sensors were numerically investigated using the Finite Difference Eigenmode method. A rib waveguide-based sensor demonstrates increased sensitivity compared to a strip waveguide-based sensor. This work contributes to developing robust and waveguide integrated sensors for applications in next-generation electrolytic and biosensors.

In this paper we study the gas sensing performance of a compact silicon photonics Mach-Zehnder interferometer (MZI) with a coiled sensing arm. A partially exposed sensor was fabricated using deep UV lithography, with a process resolution of 248 nm. Testing with inert gases, He and N$_{2}$, resulted in a measured sensitivity and limit of detection of $\sim\!$1458 nm/RIU and $\sim$8.5×10-5 RIU, respectively, in a sensing volume of 1.852 picoliters. The temperature sensitivity of the sensor was 166 pm/$^\circ$C and the inclusion of a cladded ring-resonator, post-MZI, allowed resolving the temperature drift due to gas flow. In order to further enhance the overlap of the optical mode with the measurand and thus the sensitivity, a suspended MZI was designed and simulated with an expected sensitivity of $\sim$5500 nm/RIU, for wavelengths around 1550 nm and a temperature of 300 K.

We experimentally demonstrate a compact and power efficient ( P π = 21 mW) silicide-based thermo-optic phase shifter of length 96 µm for photonic neural networks.

We experimentally demonstrate wavelength-independent couplers based on an asymmetric Mach-Zehnder interferometer on a monolithic silicon-photonics platform in a state-of-the-art CMOS foundry. The devices are also designed to exhibit fabrication tolerant performance for arbitrary splitting ratios. We have developed a semi-analytical model to optimize the device response and the reliability of the model is benchmarked against 3D-FDTD simulations. Experimental results are consistent with the simulation results obtained by the model and show uniform performance across different wafer sites with a standard deviation for the splitting ratio of only 0.6% at 1310 nm wavelength. The maximum spectral deviation of the splitting ratio (3-dB splitter) is measured to be 1.2% over a wavelength range of at least 80 nm and the insertion loss ranges from 0.08 to 0.38 dB. The wavelength-independent coupler has a compact footprint of 60 × 40 μ m 2.

We experimentally demonstrate fabrication-tolerant, arbitrary power splitters based on a Mach-Zehnder Interferometer (MZI). The devices exhibit maximum splitting ratio deviation of 1.2%, and maximum insertion loss of only 0.36 dB across the O-band.