Optical Review最新文献

In this study, we present a through-focus re-radiation simulation aimed at detecting scattering from semiconductor structures. We employ the beam synthesis propagation (BSP) module within the finite-difference time-domain (FDTD) method, optimizing the simulation of optical systems by reducing time and computational resources typically required for imaging and illumination. To validate the approach, we simulated scattering from Silicon nitride (Si₃N₄) lines on a silicon (Si) substrate with various defect sizes and types at a 193 nm wavelength. The results demonstrated the detection of specific defect signals and identified the limitations of detectable defect sizes. These findings are intended to serve as pre-processing data for predicting outcomes in through-focus scanning optical microscopy (TSOM) imaging.

Injection-molded lenses have an inhomogeneous stress-induced birefringence that can degrade optical performance. This paper presents a new approach for measuring and analyzing inhomogeneous anisotropic samples. The birefringence distribution is characterized by 3D index ellipsoids, and a tomographic reconstruction of this 3D distribution is developed from a linear line projection relationship between the spatially varying index ellipsoids and tomographic polarimetry. This forward representation enables a tensor-valued backprojection for reconstructing the birefringence distribution of an inhomogeneous anisotropic sample. In this approach, each index ellipsoid is represented by a Hermitian matrix, and the 3D birefringence distribution is defined as the distribution of these matrices. This paper is centered on the introduction of the fundamental algorithm and the presentation of a general solution by applying the Radon transform and the backprojection to a tensor field, without requiring specific parameters such as stress fields. Consequently, the computational approach presented in this paper demonstrates that, using 60 tomographic views, reconstruction errors for parameters that characterize spatially varying index ellipsoids remain less than 5%. Here, the error is defined as the ratio of reconstruction variation to the respective maximum values of the original distributions.

The demand for fast, accurate, and cost-effective methods for three-dimensional shape and color measurements has been increasing. Ideally, both the shape and color of an object should be obtained in a single shot. Color fringe projection profilometry allows single-shot 3D shape measurement; however, it faces challenges when applied to colored objects. The fringe patterns are attenuated, leading to inaccuracies in shape measurement, and the fringes obscure the object's color information. This study proposes a novel approach to address these challenges by using a deep learning-based ResUNet model. Our method uses two independently trained ResUNets to correct fringe distortions for improved shape measurement accuracy and to remove fringe patterns for color information extraction from the same captured images. The simulation and experimental results demonstrate the effectiveness and applicability of this approach for single-shot 3D shape and color measurements.

In various sports, the motion information of athletes is often measured for monitoring and evaluation, and the direct use of common optical equipment to determine the position and orientation of moving objects according to geometric information in a scene has become an important research topic in image understanding. As many sports grounds have a center circle and halfway line, we propose an algorithm that first obtains constraints on the image of the circle center by using homography based on the geometric properties of the circle perimeter and corresponding circumferential angle. Then, the vanishing line is obtained from the image of the circle center and the complete circle image based on the pole-polar relation with respect to the camera internal parameters. By decomposing the circle image, the camera external parameters are obtained to determine the homography matrix from a spatial point to an image point. The camera at the edge of the moving field is calibrated according to the duality of the conic and homography matrices. Using the homography matrix between a point on the moving ground plane and the corresponding image point, the coordinates of the measured point can be recovered to estimate the pose (i.e., position and orientation) of a moving target.

This study investigates the entanglement and squeezing characteristics of light generated by a non-degenerate, coherently driven three-level laser in an open cavity, coupled to a two-mode vacuum reservoir through a single-port mirror. By normal ordering the noise operators, we simplified the calculations and derived the evolution equations for the atomic operators using the master equation. From the steady-state solutions, we determined the average of the photon number, the quadrature variance of radiation, entanglement, the normalized second-order correlation of the cavity radiation, the linear correlation coefficient between the two modes, and fluctuations in intensity difference. Our findings indicate that higher spontaneous emission rates significantly decrease the average photon number, while the amplitude of the pumping mode interacting with the parametric amplifier ((varepsilon)) increases it. Enhanced squeezing is observed with increasing ((varepsilon)), reaching a peak at (varepsilon = 0.03) ((72.6%)). Moreover, spontaneous emission enhances squeezing. A direct correlation between squeezing and entanglement is found, with greater squeezing associated with increased entanglement. These insights have significant implications for advancing quantum technologies, such as quantum communication, where controlled squeezing and entanglement improve secure communication channels and signal-to-noise ratios, quantum computing, where they enhance error correction protocols and gate operation efficiencies, and quantum sensing, where they increase sensitivity for more precise measurements of physical quantities.

A novel self-powered ultraviolet (UV) photodetector (PD) based on a CuI/MgZnO heterojunction modified by an ultrathin Cu₂O layer has been fabricated by the successive ionic layer adsorption and reaction (SILAR) method. Compared with the CuI/MgZnO self-powered PD, the optimised heterojunction PD (CuI/Cu₂O/MgZnO) exhibits significantly improved self-powered properties. Under 325 nm UV light at an intensity of 450 µW/cm², the CuI/Cu₂O/MgZnO heterojunction PD shows exceptional photoelectric performance, featuring a high photo-to-dark current ratio of 1611, a large responsivity of 48.43 mA/W, and rapid rise and decay times of 261 ms and 890 ms, respectively, without any external power supply. Incorporating the Cu₂O interface layer results in notable enhancements in responsivity and detectivity compared to the heterojunction without the Cu₂O layer. This improvement is attributed to heterojunction interface contact, energy band engineering, and the tunneling effect. The Cu₂O layer expands the depletion zone and promotes charge separation. Due to its thinness, charges can tunnel through the Cu₂O layer from one metal electrode to another. Furthermore, the interfacial Cu₂O layer can alter the valence band offset and the conduction band offset of the p-CuI/n-MgZnO junction, enhancing carrier transport between MgZnO and CuI. These results lay the groundwork for using self-powered MgZnO-based heterojunction photodetectors in light-based devices in the future. They also demonstrate the potential of designing novel heterojunctions to create high-performance self-powered PDs for UV detection.

In the industrial production of steel materials, various complex defects may appear on the steel surface owing to the influence of environmental and other ambient factors. These defects are often accompanied by large amounts of background texture information. Especially, some defects with the low resolution and small size are prone to false alarms and missing detections. Aiming to address the issues of these specific defects, this paper proposes a bidirectional cross-scale feature fusion network combined with non-stridden convolution for steel surface defect detection. First, to improve the model’s inference speed and reduce the number of parameters, a simple yet effective convolution (PConv), the core component of FasterNet, is introduced in the feature extraction module instead of the traditional ResNet operator. Second, the bidirectional crossing (BiC) module is embedded to construct a bidirectional cross-scale feature fusion network (BiCCFM), which provides more accurate localization clues to enhance the feature representation on small targets. Finally, combined with non-stridden convolution, the SPD-Conv module is developed to aggregate the detection performance of small targets in low-resolution images. Comprehensive experimental results on the public NEU-DET dataset validate the effectiveness of the embedded modules and the proposed model. Compared with other state-of-the-art methods, the proposed model achieves the best accuracy (74.2% mAP @ 0.5) while maintaining a relatively small number of parameters.

Background

Two-dimensional mapping based on reflectance measurements is a promising approach for evaluating the characteristics of breast cancer and its response to chemotherapy. However, three-dimensional imaging should enable extraction of more accurate values and distributions of the parameters, potentially increasing the validity of the assessment. Therefore, we have been developing reflectance diffuse optical tomography (RDOT) for three-dimensional imaging of breast cancer using time-domain reflectance measurements.

Methods

We performed RDOT imaging on 64 patients with breast cancer and acquired three-dimensional images of total hemoglobin concentration (tHb) and tissue oxygen saturation (StO₂) at the lesion and a symmetrical area in the normal breast. The correlation between tHb and the skin-to-chest wall distance was investigated to evaluate the effect of the chest wall. We also examined correlations of tHb with tumor depth and tumor thickness. In addition, we measured patients with breast cancer before and after they underwent neoadjuvant chemotherapy and compared the results.

Results

The tHb value increased in both cancerous and normal breasts as the skin-to-chest wall distance decreased. Cancerous breasts showed higher tHb and lower StO₂ than normal breasts. tHb showed a negative correlation with tumor depth and a positive correlation with tumor thickness. Long-term monitoring of lesions during chemotherapy demonstrated that tHb decreased as the tumor size reduced with chemotherapy.

Conclusions

We confirm that the RDOT has potential for a novel examination method with no pain and no radiation exposure; however, the reconstructed image is still affected by chest wall, tumor size, and tumor depth.

With the development of deep learning and structured light streak projection techniques in three-dimensional (3D) imaging, research on the direct reconstruction of 3D shapes from single-streak images has attracted much attention. However, accurately reconstructing 3D shapes is particularly challenging when dealing with objects with specular reflective surfaces. To address this problem, this paper proposes an innovative multi-stage deep learning method that combines the pix2pix adversarial network and a modified version of the DC-HNet architecture. The technique aims to accurately reconstruct 3D shapes from streaked images by eliminating highlights in specular reflection regions through a staged process first. Specifically, the pix2pix adversarial network is first used to eliminate highlights and generate streak images without specular reflections; subsequently, the improved DC-HNet network is further processed to accurately deduce the phase distribution information of the object from the streak images with the elimination of highlights, and then reconstruct the 3D shape. Compared with the traditional self-encoder-based convolutional neural network (CNN) model, the multi-stage approach proposed in this paper significantly improves the accuracy of 3D shape reconstruction by separating the two key steps of highlight elimination and phase derivation and combining them with multi-scale feature enhancement. In this paper, the method’s effectiveness is verified on experimental data, and the results show that the proposed method provides a significant improvement in 3D shape prediction accuracy compared with the existing U-Net network and MultiResUet network. These findings not only demonstrate the innovation and robustness of the proposed method but also show its potential in scientific research and engineering applications.

Novel hexagonal form factor for evaluating chromaticity distribution of color speckle using red, green, and blue lasers is proposed. The measured color speckle distribution data were plotted on CIE 1931 chromaticity diagram. Only the small amount of outer rim data was extracted from the whole data of the chromaticity distribution to introduce the novel form factor. Specifically, the highest 1% and the lowest 1% of each of red, green, and blue monochromatic speckle data were picked up to form six outer rim regions. The average chromaticity coordinates of the six regions corresponding to hexagonal apexes are appropriate for evaluating the form factor of the chromaticity distributions, clarifying the distinctive difference between the measured and the calculated color speckle distributions.