In photonic crystal slab (PCS) structures, the bound states in the continuum (BICs) and circularly polarised states (dubbed C-points) are critical topological polarisation singularities in momentum space that have garnered significant attention owing to their novel topological and optical properties. In this study, we engineered a novel PCS imager featuring two C-points with opposite chirality through symmetry breaking, resulting in maximal asymmetric transmission responses characterised by near-unity circular dichroism (CD) values. By harnessing the chiral selectivity of the C-points, a high-CD PCS imager can provide two sets of optical transfer functions (OTFs) to facilitate both edge detection and bright-field imaging. Notably, one set of OTFs was finely tuned to a Lorentzian line shape to achieve perfect edge detection. We developed a multifunctional imaging system by integrating a PCS imager into a traditional optical system. Both theoretical and experimental demonstrations confirmed that this system provides bright-field and edge-enhanced images with micrometer-scale resolution. Furthermore, these two independent functions can be easily switched by altering the circular polarisation state of the light source.
In this study, a ray tracing model based on the law of reflection in vector form was developed to obtain the design parameters of multipass cells (MPC) with dense spot patterns. Four MPCs with distinct patterns were obtained using an established mathematical model. An MPC with a four-concentric-circle pattern exhibited the longest optical path length (OPL) of approximately 38 m and an optimal ratio of optical path length to volume (RLV) of 13.8 cm-2. A light-induced thermoelastic spectroscopy (LITES)-based methane (CH4) sensor was constructed for the first time using the developed optimal MPC and Raman fiber amplifier (RFA). A novel trapezoidal-tip quartz tuning fork (QTF) was used as the detector to further improve the sensing performance. The CH4-LITES sensor exhibited an excellent linear response to optical power and CH4 concentration. The minimum detection limit (MDL) of the CH4-LITES sensor reached 322 ppb when the output optical power of the RFA was 350 mW. The Allan deviation of the system indicated that the MDL decreased to 59.5 ppb when the average time was increased to 100 s.
One of the challenges in the field of multi-photon 3D laser printing lies in further increasing the print speed in terms of voxels/s. Here, we present a setup based on a 7 × 7 focus array (rather than 3 × 3 in our previous work) and using a focus velocity of about 1 m/s (rather than 0.5 m/s in our previous work) at the diffraction limit (40×/NA1.4 microscope objective lens). Combined, this advance leads to a ten times increased print speed of about 108 voxels/s. We demonstrate polymer printing of a chiral metamaterial containing more than 1.7 × 1012 voxels as well as millions of printed microparticles for potential pharmaceutical applications. The critical high-quality micro-optical components of the setup, namely a diffractive optical element generating the 7 × 7 beamlets and a 7 × 7 lens array, are manufactured by using a commercial two-photon grayscale 3D laser printer.
The interactions between ultrafast lasers and materials reveal a range of nonlinear transient phenomena that are crucial in advanced manufacturing. Understanding these interactions during ultrafast laser ablation requires detailed measurements of material properties and structural changes with high temporal and spatial resolutions. Traditional spatiotemporal imaging techniques relying on reflective imaging often fail to capture comprehensive information, resulting in predominantly qualitative theoretical models of these interactions. To overcome this limitation, we propose a dual-modal ultrafast microscopy system that combines two-dimensional reflectivity and three-dimensional topography imaging. By integrating pump-probe techniques with an interferometric imaging system, impressive spatiotemporal resolutions of 236 nm and 256 fs were achieved. Furthermore, using this system, we successfully examined the dynamics of laser-induced periodic surface structure formation, strengthening, and erasure on Si surfaces. The results demonstrate that the dual-modal spatiotemporal imaging technique can serve as a robust tool for the comprehensive analysis of ablation dynamics, facilitating a deeper understanding of the fundamental physics involved and enabling more accurate optimisation of ultrafast laser fabrication processes.
Defect inspection is critical in semiconductor manufacturing for product quality improvement at reduced production costs. A whole new manufacturing process is often associated with a new set of defects that can cause serious damage to the manufacturing system. Therefore, classifying existing defects and new defects provides crucial clues to fix the issue in the newly introduced manufacturing process. We present a multi-task hybrid transformer (MT-former) that distinguishes novel defects from the known defects in electron microscope images of semiconductors. MT-former consists of upstream and downstream training stages. In the upstream stage, an encoder of a hybrid transformer is trained by solving both classification and reconstruction tasks for the existing defects. In the downstream stage, the shared encoder is fine-tuned by simultaneously learning the classification as well as a deep support vector domain description (Deep-SVDD) to detect the new defects among the existing ones. With focal loss, we also design a hybrid-transformer using convolutional and an efficient self-attention module. Our model is evaluated on real-world data from SK Hynix and on publicly available data from magnetic tile defects and HAM10000. For SK Hynix data, MT-former achieved higher AUC as compared with a Deep-SVDD model, by 8.19% for anomaly detection and by 9.59% for classifying the existing classes. Furthermore, the best AUC (magnetic tile defect 67.9%, HAM10000 70.73%) on the public dataset achieved with the proposed model implies that MT-former would be a useful model for classifying the new types of defects from the existing ones.
Mode division multiplexing (MDM) using multimode fibers (MMFs) is key to meeting the demand for higher data rates and advancing internet technologies. However, optical transmission within MMFs presents challenges, particularly due to mode crosstalk, which complicates the use of MMFs to increase system capacity. Quantitatively analyzing the output of MMFs is essential not only for telecommunications but also for applications like fiber sensors, fiber lasers, and endoscopy. With the success of deep neural networks (DNNs), AI-driven mode decomposition (MD) has emerged as a leading solution for MMFs. However, almost all implementations rely on Graphics Processing Units (GPUs), which have high computational and system integration demands. Additionally, achieving the critical latency for real-time data transfer in closed-loop systems remains a challenge. In this work, we propose using field-programmable gate arrays (FPGAs) to perform neural network inference for MD, marking the first use of FPGAs for this application, which is important, since the latency of closed-loop control could be significantly lower than at GPUs. A convolutional neural network (CNN) is trained on synthetic data to predict mode weights (amplitude and phase) from intensity images. After quantizing the model’s parameters, the CNN is executed on an FPGA using fixed-point arithmetic. The results demonstrate that the FPGA-based neural network can accurately decompose up to six modes. The FPGA’s customization and high efficiency provide substantial advantages, with low power consumption (2.4 Watts) and rapid inference (over 100 Hz), offering practical solutions for real-time applications. The proposed FPGA-based MD solution, coupled with closed-loop control, shows promise for applications in fiber characterization, communications, and beyond.
Monolithic multi-freeform optical structures play significant roles in advanced optical systems by simplifying system structures and enhancing optoelectronic performance. However, manufacturing and measurement present significant challenges, which require the simultaneous assurance of form quality and relative positioning of multiple functional surfaces. Consequently, a deterministic form-position deflectometric measuring method is proposed based on Bayesian multisensor fusion, which effectively overcomes the inherent limitation of deflectometry in absolute positioning. Calibration priors were marginalised in the measurement model to improve fidelity, and a fully probabilistic measurement framework was proposed to eliminate numerical bias in conventional sequential optimisation approaches. Finally, a geometric-constraint-based registration method was developed to evaluate the form-position quality of freeform surfaces. The experimental results demonstrated the measurement accuracy could achieve a level of one hundred nanometres for surface forms and a few microns for surface positions.
Endoscopes are indispensable for minimally invasive optical applications in medicine and production engineering. The smallest lensless endoscopes often use digital optics to compensate the intrinsic distortions of light propagation of multimode or multicore fibers. However, due to the wavelength dependency of the distortion, the approach is restricted to a narrow spectral range, which prevents multispectral imaging modalities. We employ a spatial light modulator with a high stroke above 2
, to generate a hologram which minimizes overall phase distortion for multiple spectral bands. This enables lensless multicore fiber single-shot RGB endoscopy, for the first time in the world. Many applications in advanced manufacturing and biomedicine such as in vivo tissue classification are enabled.
Quartz tuning forks have been recently employed as infrared photodetectors in tunable laser diode spectroscopy because of their high responsivities and fast response time. As for all sensitive elements employed for photodetection, the main drawback is the limited bandwidth of their absorption spectrum. For quartz crystals, the high absorptance for wavelengths above 5 µm guarantees excellent performance in the mid-infrared range, that cannot be easily extended in the visible/near-infrared range because of its transparency from 0.2 to 5 µm. In this work, we report on the development of a laser surface functionalization process to enhance the optical absorption of quartz crystals, named hereafter Black Quartz, in the 1-5 µm spectral range. Black Quartz consists of surface modification of quartz crystal by ultra-fast-pulsed-laser-processing to create localized matrices-like patterns of craters on top. The surface modification decreases the transmittance of quartz in the 1-5 µm range from > 95% down to < 10%, while the transmittance above 5 µm remains unchanged. The Black Quartz process was applied on two quartz-tuning-forks mounted in a tunable laser diode spectroscopy sensor for detecting two water vapor absorption features, one in the near infrared and the other one in the mid-infrared. A comparable responsivity was estimated in detecting both absorption features, confirming the extension of the operation in the near-infrared range. This works represents an important and promising step towards the realization of quartz-based photodetector with high and flat responsivity in the whole infrared spectral range.
This review considers the modern industrial applications of augmented reality headsets. It draws upon a synthesis of information from open sources and press releases of companies, as well as the first-hand experiences of industry representatives. Furthermore, the research incorporates insights from both profile events and in-depth discussions with skilled professionals. A specific focus is placed on the ergonomic characteristics of headsets: image quality, user-friendliness, etc. To provide an objective evaluation of the various headsets, a metric has been proposed which is dependent on the specific application case. This enables a comprehensive comparison of the various devices in terms of their quantitative characteristics, which is of particular importance for the formation of a rapidly developing industry.
Transparent objects are widely used in various fields, leading to increasing demand for methods of measuring them. However, the measurement of such objects has always been challenging owing to the intricate refraction and reflection phenomena they exhibit. Given that traditional contact measurement methods can damage transparent objects, the use of non-destructive measurement techniques, particularly those based on optical principles, is considered preferable. As a result, various non-destructive measurement methods have been developed for transparent objects by leveraging the unique characteristics of light, and a comprehensive review is imperative for exploring these innovative methods and their potential applications. This review accordingly begins by elucidating the necessity of measuring transparent objects and exploring the concept of transparency. Next, an overview of various non-destructive optical measurement techniques spanning macro-, micro-, and general-scale applications is presented, followed by a discussion of their respective advantages and limitations. Finally, the paper concludes by outlining future directions for potential advancements in the field. This review is expected to serve as a valuable resource for newcomers in the field of transparent object measurement and assist researchers seeking to integrate these techniques into interdisciplinary studies.
Light-based additive manufacturing holds great potential in the field of bioprinting due to its exceptional spatial resolution, enabling the reconstruction of intricate tissue structures. However, printing through biological tissues is severely limited due to the strong optical scattering within the tissues. The propagation of light is scrambled to form random speckle patterns, making it impossible to print features at the diffraction-limited size with conventional printing approaches. The poor tissue penetration depth of ultra-violet or blue light, which is commonly used to trigger photopolymerization, further limits the fabrication of high cell-density tissue constructs. Recently, several strategies based on wavefront shaping have been developed to manipulate the light and refocus it inside scattering media to a diffraction-limited spot. In this study, we present a high-resolution additive manufacturing technique using upconversion nanoparticles and a wavefront shaping method that does not require measurement from an invasive detector, i.e., it is a non-invasive technique. Upconversion nanoparticles convert near-infrared light to ultraviolet and visible light. The ultraviolet light serves as a light source for photopolymerization and the visible light as a guide star for digital light shaping. The incident light pattern is manipulated using the feedback information of the guide star to focus light through the tissue. In this way, we experimentally demonstrate that near-infrared light can be non-invasively focused through a strongly scattering medium. By exploiting the optical memory effect, we further demonstrate micro-meter resolution additive manufacturing through highly scattering media such as a 300-μm-thick chicken breast. This study provides a concept of high-resolution additive manufacturing through turbid media with potential application in tissue engineering.
Femtosecond 3D-printing offers tantalizing avenues for miniaturization and integration of micro optical systems. Available photoresists, however, restrain their utility in liquid immersion, especially in media with refractive indices larger than n = 1.33, such as glues or biomedical fluids. We present monolithic 3D-printed immersion optics, equipped with compact microfluidic sealing to protect the micro optical device from intrusion of liquid immersion media. We experimentally demonstrate diffraction limited performance in water, silicone-, and immersion oil, for a tailored aspherical-spherical doublet with a numerical aperture of NA = 0.625 and a footprint as small as a single mode optical fiber. Such compact monolithic immersion micro optics yield high potential to advance miniaturization for in situ biomedical sensing and robust coupling between fibers and photonic integrated circuits.
ISSN 2689-9620 EISSN 2831-4093
Indexed by:
- ESCI
- DOAJ
- Scopus
- Google Scholar
- CNKI
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2021, 2(3): 350-369. doi: 10.37188/lam.2021.024
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2023, 4(4): 519-542. doi: 10.37188/lam.2023.031
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2025, 6(1): 5-13. doi: 10.37188/lam.2025.001
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2021, 2(3): 313-332. doi: 10.37188/lam.2021.020
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2021, 2(1): 59-83. doi: 10.37188/lam.2021.005
