In the selective laser melting (SLM) process, the stability of the melt pool plays a crucial role in determining the surface and internal quality of the formed parts. To address the limitations imposed by unstable factors during the SLM process, introducing in-process monitoring technology is essential. The technology can reveal the relationship between the melt pool radiation signals and the quality of the formed parts. By combining these signal characteristics with process parameters, it is possible to control the quality of the formed parts, which is of great significance to the advancement of SLM technology.

Based on the principle that metal powder absorbs laser energy and generates optical radiation upon melting during the SLM process, this study developed a coaxial in-situ monitoring device for SLM, utilizing photodiodes. This device captured the radiation time-series signals of the high-temperature melt pools during the printing process that was carried out under an unprotected atmosphere. After preprocessing the collected melt pool radiation signals, we examined the mapping relationships among process parameters, forming performance, and optical radiation signals intensity. Subsequently, signal processing techniques, such as fast Fourier transform (FFT) and wavelet transform, were employed to analyze the characteristic changes of the optical signals in the time-frequency domain. This study provides theoretical guidance for applying process monitoring in SLM.

Laser power has a direct effect on the time-domain mean of the optical signal, which in turn influences the mechanical properties and surface roughness of the formed parts. However, the scanning speed shows minimal impact on the mean value of the optical signal intensity. FFT and wavelet transform analyses revealed a strong relationship between the appearance and distribution of characteristic peaks and scanning speed. The study also shows that reducing input energy can improve the stability of the melt pool and significantly suppress the splashing phenomenon caused by the high-energy beam impact. This is particularly significant for improving both the surface and internal quality of SLM-formed parts.

(1) At low laser power, the surface melt of the sample is discontinuous, and an obvious spheroidization phenomenon occurs. As the laser power increases, the surface melt becomes continuous and complete. As scanning speed increases, the amount and size of residual splashes on the sample surface and powder bed gradually decrease, although this may lead to insufficient powder melting. When this occurs, the melt becomes discontinuous, cracks expand, and the density of the samples is significantly reduced. At lower scanning spacing, a severe oxidation reaction occurs during the forming process. Additionally, as scanning spacing increases, gaps form between each melt, and more pores and incomplete fusion defects appear.

(2) The amplitude variation of the time-domain signals is mainly influenced by laser power rather than scanning speed. As laser power increases, the amplitude of the optical signal increases, but its stability decreases. As scanning distance increases, the mean and standard deviation of the optical signal intensity initially increase and then decrease.

(3) When scanning speed is below 1000 mm/s, increasing the laser power leads to a decrease in surface roughness with an increase in the average optical signal intensity. As the optical signal intensity increases, the porosity of the sample decreases significantly. At optimal laser power, the surface roughness and porosity show a trend of initially decreasing and then increasing with the increase in scanning speed, while the average optical signal remains relatively stable. At different scanning spacing, surface roughness and porosity decrease as the amplitude of the optical signal increases. Meanwhile, the average density and signal intensity mean of each process group decrease with decreasing laser power. At the same scanning speed, the coefficient of variation also decreases with decreasing laser power. Increasing scanning spacing causes the density of the sample and the optical signal intensity mean to follow a trend of first increasing and then decreasing. The performance of the tensile samples increases with an increase in the mean radiation signal intensity of the melt pool.

(4) The frequency range of the optical signals is primarily concentrated in the low-frequency band. Additionally, spectral peaks in the low-frequency part distribute at certain intervals. The periodic signals exhibited in the spectrum are closely related to the generation of splashing. Based on wavelet transform, we find that the appearance and distribution of the characteristic peaks in the frequency domain signal of the melt pool are not significantly related to laser power, but are closely tied to scanning speed. Further analysis through three-layer wavelet packet decomposition reveals that the signal is mainly concentrated in the frequency range of 0?12500 Hz. The kurtosis value of the time-domain signals decreases with increasing scanning speed. The splashing induced by high-energy beam impact is significantly suppressed as scanning speed increases.

LiDAR imaging in foggy conditions is essential for applications such as autonomous driving, aviation navigation, and surveillance. However, traditional LiDAR systems face significant limitations in such environments due to the scattering and absorption of laser beams by fog, resulting in reduced detection range and degraded image quality. Single photon avalanche diode (SPAD) technology, with its exceptional sensitivity and high resolution, has emerged as a promising solution. SPAD systems can operate under extremely low light conditions. When combined with time-correlated single photon counting (TCSPC), they can effectively detect and process individual photon signals. This capability enables reliable detection and imaging even in low-visibility environments like fog and haze. Therefore, investigating the performance of SPAD-based LiDAR systems in foggy conditions is crucial for advancing these applications.

In this paper, we utilize high-sensitivity SPAD combined with the TCSPC method to extract the depth and intensity information of targets in foggy environments. Monte Carlo simulations are conducted to analyze the transmission characteristics of laser beams in fog, providing a robust scientific foundation for this study. A Gamma distribution is used to model the scattering peaks caused by fog, while a Gaussian distribution is applied to represent peaks generated by target reflections. To enhance image quality, the Levenberg-Marquardt (LM) algorithm is combined with total variation (TV) regularization, significantly improving target reconstruction accuracy and clarity in fog conditions.

The photon echo data collected in foggy environments are analyzed using a Gamma-Gaussian mixture model to reconstruct depth and intensity images. Three-dimensional image reconstruction (Fig. 7) is performed using the peak value method, maximum likelihood estimation (MLE) algorithm, and the proposed LM-TV algorithm. Comparative analysis demonstrates that the LM-TV algorithm outperforms traditional methods, reducing the root mean square error (RMSE) of the depth image by 1.0231 and increasing the structural similarity index (SSIM) of the intensity image by 0.5485 (Table 1). These results highlight the effectiveness of the LM-TV method in fog penetration imaging, delivering more accurate and robust target reconstruction.

In this paper, TCSPC technology is utilized to obtain photon echo data in the time domain under foggy conditions. A Gamma-Gaussian mixture model is employed to separate fog echo signals from target reflections, enabling precise depth and intensity to be extracted using the LM algorithm. Compared to the peak value method, the LM algorithm reduces the RMSE of the reconstructed depth image by 0.9475 and improves the SSIM of the reconstructed intensity image by 0.4720. The integration of TV regularization with the LM algorithm further reduces the RMSE of the depth image by an additional 0.0756 and enhances the SSIM by 0.0765. When compared to the MLE algorithm, the combined LM-TV method achieves a reduction in RMSE of 0.4788 and an improvement in SSIM by 0.4563. These findings demonstrate that the hybrid LM-TV algorithm significantly outperforms traditional methods, offering a more accurate and robust solution for target reconstruction in foggy environments.

Solar-induced chlorophyll fluorescence (SIF) is a valuable metric for assessing photosynthesis and vegetation stress. However, as SIF radiance constitutes less than 3% of the reflected canopy radiance, the spectral resolution of a spaceborne SIF detector should be below 0.3 nm. To ensure adequate signal-to-noise ratio (SNR), current spaceborne SIF imagers typically achieve spatial resolutions above 1 km. European Space Agency’ FLEX (Fluorescence explorer) mission recommends spatial resolution below 300 m, particularly for monitoring field and forest areas in Europe. The complex terrain and vegetation types in China, however, demand even higher spatial resolutions. In this paper, we propose a mid-resolution ultraspectral imager (MIRUS), designed for satellite-based SIF detection at a spatial resolution of 100 m. To evaluate the SIF retrieval performance of MIRUS, we develop a model that leverages SIF imaging spectrometer (SIFIS) data to calculate SIF retrieval accuracy.

Given the weak SIF radiance relative to canopy reflectance, the design specifications for MIRUS are shown in Table 1. MIRUS employs a low F# optic and a Littrow-Offner spectrometer to improve irradiance on the focal plane array (FPA) and reduce chromatic aberration, as shown in Fig. 2. The modulation transfer function (MTF) is optimized to be greater than 0.9, as shown in Fig. 3, while smile and keystone distortions are controlled to below 1.0% pixels and 3.3% pixels, respectively, as shown in Table 3. The spectral resolution is set at 0.3 nm, with a convex grating designed using rigorous coupled-wave analysis (RWCA) (Fig. 5), achieving an average diffraction efficiency of 0.7 (Fig. 7). The mechanism is designed as shown in Fig. 8. A prototype of MIRUS is produced, combining a telescope, a spectrometer, and an FPA. The prototype’s performance, including instrument line shape (ILS), spectral resolution, smile, keystone, and SNR, is tested, with results shown in Figs. 9?13.

To assess MIRUS’s SIF detection performance, we build a model relating SIF retrieval accuracy to the SNR of the spectral imager. The spectral range and resolution of the SIFIS on the Goumang satellite are comparable to the designed performance of MIRUS, with SIFIS achieving a maximum spatial resolution of 0.375 km×0.800 km in non-binning mode. In addition, we analyze 48 SIFIS image orbits from January to October in 2023, covering diverse environments such as tropical rainforests, savannas, deserts, and polar regions. Using singular value decomposition (SVD) in the 743?758 nm range, we measure retrieval errors across different radiance levels (Fig. 17) and develop an SNR model for SIFIS data (Fig. 16). The radiance of MIRUS within the same wavelength range is simulated using the MODTRAN 6.0 model, based on MIRUS’s orbital parameters and typical atmospheric, aerosol, and ground albedo parameters, as shown in Table 4. Subsequently, the SNR for MIRUS is calculated from this radiance, as shown in Fig. 16. Finally, the SIF retrieval accuracy for MIRUS is evaluated using a polynomial function of SNR, with results shown in Fig. 18. The relative errors for SIF retrieval are 1.22%?1.38% for 100 m GSD mode and 0.69%?0.86% for the 200 m GSD mode.

SIF serves as a “probe” for photosynthetic activity. “The remote sensing of chlorophyll fluorescence is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and prospects for diverse applications,” as noted by G. H. Mohammed. Due to the weak SIF radiance, it must be captured with an ultraspectral imager. Considering the imaging SNR, the spatial resolution of SIF radiance retrieved by spaceborne instruments typically exceeds 1 km. In this paper, we propose a mid-resolution spaceborne SIF detector featuring a small F# TMA and a Littrow-Offner spectrometer. The high-groove-density convex grating is designed using rigorous coupled-wave analysis (RCWA), resulting in significantly greater irradiance on the FPA compared to SIFIS. A prototype is produced and tested, achieving a full width at half maximum (FWHM) of 0.3 nm, a spectral sampling interval (SSI) of 0.1 nm, a smile distortion of less than 0.0035 nm, a keystone of under 0.06 pixel, and an SNR exceeding 206 at 10 mW·m^-2·sr^-1·nm^-1. To evaluate the SIF retrieval accuracy of MIRUS, we develop a method to estimate accuracy based on the instrument’s SNR. The designed spectral performance of SIFIS matches that of MIRUS, and the spatial resolution of SIFIS is comparable to MIRUS. A polynomial relationship between SNR and SIF retrieval accuracy is established using radiance data from SIFIS. After calculating the typical radiance received by MIRUS, we use the SNR model to determine its typical SNR. Finally, the SIF retrieval accuracy is calculated using the polynomial, yielding relative errors of 1.22%?1.38% for the 100 m GSD mode and 0.69%?0.86% for the 200 m GSD mode, comparable to SIFIS performance.

Laser cladding, a new type of surface strengthening and repair technology, has been widely used in the automobile and aerospace fields for parts repair. Owing to the limitations of the laser spot size and robot travel range, meeting the industrial production needs of large-area surface modification and remanufacturing is challenging for single-pass cladding. During multipass cladding, a high-energy-density laser beam induces the secondary melting and solidification of alloy powder and the substrate surface coating. This process alters heat conduction and the melt flow mode inside a molten pool, resulting in an instantaneous and complex dynamic evolution of the molten pool. Numerical simulation can deeply analyze such complex phenomena inside a molten pool, offering better insights into the influence mechanism of the melt flow and temperature distribution on the molding and quality of the cladding layer. In this study, we simulated the multipass cladding process, revealed the evolution of the temperature and flow fields, studied the influence of the overlap ratio on cladding layer flatness, and observed the morphology and microstructure of the cladding layer under the optimal overlap ratio through experimental methods. The results of this study will provide a valuable reference for analyzing the evolution law of the temperature and flow fields of a multipass molten pool through numerical simulation and determining the appropriate multipass cladding process parameters.

Considering the temperature and laser attenuation coefficient of the cladding powder, and the Gaussian distribution powder equation, a multiphase flow model of multipass laser cladding was established via numerical simulation. First, the evolution of the internal temperature and flow fields within a molten pool during the multipass cladding process was analyzed. Second, numerical models for multipass cladding under different overlap ratios of 25%?60% were established to study the effects of the overlap ratio on the internal flow field of multipass molten pools, and the surface flatness values of the cladding layer under different overlap ratios were obtained; moreover, the overlap ratio yielding a highly flat cladding layer surface was determined. Finally, the laser cladding experiment on a 42CrMo substrate was performed under the optimal overlap ratio, and the microstructure of the cladding layer was observed.

In the first cladding pass, the temperature gradient distribution of a molten pool exhibits characteristics of high in the middle and low on both sides. During subsequent laser cladding, this gradient gradually flattens as the number of cladding passes increases (Fig. 6). Owing to the preheating effect from previous cladding passes, initial temperatures at three observation points gradually increase with each additional cladding sequence. In addition, the secondary peaks in the temperature curves at observation points D and E align with the peak temperature of subsequent measurement points. These peaks exceed the liquidus temperature of the cladding powder, indicating remelting at these points. This remelting causes the previous cladding layer to re-form into a molten pool (Fig. 9). During the first cladding pass, two stable and symmetrical counterclockwise swirls form within a molten pool under the combined action of Marangoni forces and buoyancy. In subsequent cladding passes, the lapped section of the cladding layers remelts into a molten pool. Such molten pools take on an obliquely elliptical shape, generating a gravitational component to the unlapped side, breaking the original force balance, and affecting the flow field motion law dominated by Marangoni forces. And thus the two internal swirls gradually become asymmetrical. This phenomenon becomes more pronounced as the number of cladding passes increases, resulting in changes to the microstructure and uneven topography of the cladding layer (Fig. 11). The flatness of the cladding layer first increases and then decreases as the overlap ratio increases. Flatness reaches its maximum value at a 40% overlap ratio (Table 4). At an overlap ratio of 35%, two swirls of different sizes form within a molten pool. At an overlap ratio of 45%, the overall height of the cladding layer increases, and surface tension on the overlap side is almost balanced by gravity. Consequently, only one swirl remains in the molten pool; furthermore, maintaining uniform melt flow is difficult and thus considerably decreases cladding layer flatness (Fig. 13). At an overlap ratio of 40%, the microstructure of the cladding layer transitions from planar crystals at the bottom to columnar, cellular, and equiaxed crystals toward the top. The crystal distribution is relatively uniform, and the distribution of dendrite is consistent with the simulation results (Fig. 15).

The temperature gradient of single-pass cladding shows a pattern of high in the middle and low on both sides under the Marangoni flow effect. In multipass cladding, this gradient gradually flattens with increasing the number of cladding passes. The initial and peak temperatures of the cladding layer rise with each successive pass, and when the temperature in the subsequent cladding process exceeds the liquidus temperature, remelting of the previous cladding layer occurs. In single-pass cladding, two symmetrical swirls form within a molten pool flow field under the action of Marangoni forces, which guide a melt to exhibit a cyclic flow. As the overlap ratio increases, the influence of gravity on the flow field of the molten pool increases, eventually causing the swirl on the lap side to disappear. This phenomenon changes the microstructure and causes the uneven surface morphology of the cladding layer. In addition, with the increase in the overlap ratio, the flatness of the cladding layer shows a trend of first increasing and then decreasing, and the optimal flatness value is observed at a 40% overlap ratio. Experimental results confirm that at this overlap ratio, the cladding layer exhibits a relatively flat surface with relatively uniform microstructure distribution.

Water color is a fundamental parameter for describing the optical properties of water bodies and encapsulates vital information about the aquatic environment. As the most visually direct indicator in marine surveys, water color reflects not only change in the aesthetic quality of water bodies but also plays a key role in environmental impact assessments, especially in sensitive areas. Water color is influenced by light scattering and changes in environmental conditions, closely related to factors such as chlorophyll, suspended particulate matter (SPM), and the absorption and scattering of colored dissolved organic matter. Previous research has mainly relied on the Forel-Ule index (FUI) for measuring water color. However, due to the complexity and variability of China’s coastal waters, the FUI may not capture detailed water color information or accurately represent the environmental conditions of the water body. In contrast, the hue angle (α) in the Commission internationale de l’éclairage (CIE) color system, as a continuous variable, provides a more accurate representation of water color characteristics and helps extract detailed water quality information. Monitoring water color not only provides vital information on global and regional water quality assessments but also plays a crucial role in marine environmental protection and the maintenance of ecological balance.

Based on data collected during research cruises, including Secchi depth (Z_sd), mass concentration of suspended particulate matter (SPM), and phytoplankton absorption coefficient (a_ph), we develop inversion models for Z_sd, SPM, a_ph (443), and a_ph (670) using the hue angle as the key variable. These models are validated using the leave-one-out cross validation method. Utilizing hyperspectral remote sensing reflectance (R_rs) data collected during the cruise, we perform stepwise regression analysis with SPSS software. The hue angle serves as the dependent variable, while R_rs values at the MODIS’s central spectral bands act as the independent variables. The data are divided so that 2/3 of data are used to calculate the chroma parameter K. Finally, long-term hue angle information for China’s coastal waters is obtained using satellite R_rs data. This study also compares the effectiveness of the hue angle and the FUI in characterizing changes in water color parameters based on cruise-measured data. The results reveal that the hue angle provides a more detailed and continuous representation of variations in water color parameters.

This study uses R_rs data of MODIS and SeaWiFS to obtain long-term hue angle information for China’s coastal waters, utilizing a method that extracts hue angles from multispectral R_rs (Fig. 6). The highest hue angles are recorded near the coast (around 200°), with values decreasing offshore. The Bohai Sea has the highest average hue angle (180°), followed by the Yellow Sea, which also shows the most significant seasonal variation, with offshore values ranging from 70° to 140°. The East China Sea has the lowest average hue angle (60°) and the least seasonal fluctuation. Seasonal patterns are observed, with hue angles decreasing from spring to summer, reaching their lowest in summer, then increasing in autumn and peaking in winter. In certain characteristic sea areas, the hue angle shows strong covariation with water quality parameters like mass concentrations of Chl-a and SPM. In areas with high hue angles, a significant correlation is observed between hue angles and mass concentration of SPM, while in areas with lower hue angles, strong covariation is observed among hue angles, mass concentration of SPM, and mass concentration of Chl-a (Fig. 7). Using in-situ data from the East China Sea, we develop models for several water quality parameters based on hue angles and validate their accuracy using the leave-one-out cross validation method. This approach can be applied to portable high-definition imaging devices, such as smartphones and digital cameras, to capture ocean water color images, extract hue angle information, and obtain water quality data. A comparison of the hue angle (α) and FUI reveals that the discrete nature of FUI leads to the loss of water color information (Fig. 8). In contrast, the continuous nature of the hue angle captures more detailed color data (Figs. 9 and 10).

We applied the CIE-XYZ color system and a hue angle retrieval method based on satellite multispectral remote sensing reflectance. By combining data from the MODIS and SeaWiFS satellites, a long-term dataset of hue angle for China’s coastal waters was obtained. The study reveals that the hue angle exhibits distinct spatiotemporal distribution characteristics. The Bohai Sea has the highest monthly average (180°), followed by the Yellow Sea (100°), both showing significant seasonal variability. In contrast, the East China Sea has a lower monthly average (60°) with minimal seasonal variability. In addition, there is a strong covariation between hue angles and water quality parameters. Based on cruise-measured data, we developed retrieval models for water quality parameters using hue angle. The results demonstrate that the models for transparency (R2=0.79), suspended particulate matter concentration (R2=0.90), phytoplankton absorption coefficient at 443 nm (R2=0.79), and phytoplankton absorption coefficient at 670 nm (R2=0.80) exhibit high goodness of fit and accuracy. Furthermore, we analyzed and discussed the advantages of using hue angle over traditional water color indices for representing water color information in the complex coastal waters of China. The findings suggest that the hue angle provides a more accurate and effective measure of water color, offering superior capability in conveying aquatic environmental information. This highlights the potential application value of the hue angle as a parameter for accurately expressing oceanic water environmental information.

High-spectral-resolution LiDAR (HSRL) is essential for precise detection and retrieval of aerosol optical properties, making it a valuable tool in atmospheric aerosol studies. While the HSRL technique has seen rapid advancements in ultraviolet and visible wavelengths 355 nm/532 nm, development in the near-infrared HSRL domain is constrained by the limitations of spectral discriminators. In 2017, the National Center for Atmospheric Research (NCAR) has proposed a 780 nm near-infrared micro-pulse HSRL technique using rubidium (Rb) atom absorption lines and 780 nm semiconductor lasers. This approach provides a promising solution to the challenges facing near-infrared HSRL and has become a research focal point worldwide. However, the effect of various Rb absorption cell parameters on detection errors in the 780-nm HSRL system remains unexplored. In this paper, we address this gap by analyzing the influence of Rb cell parameters, system signal-to-noise ratio (SNR), and laser frequency stability on detection results, based on the absorption spectrum of rubidium isotope (⁸⁷Rb). This study offers theoretical guidance for designing 780-nm near-infrared HSRL systems, particularly in optimizing the temperature settings of the Rb cell spectral discriminator.

We employ the Monte Carlo method in this analysis. First, the HSRL error formula is derived, and the absorption spectrum is obtained based on the hyperfine structure of rubidium atoms. An error analysis model for the 780-nm HSRL system is then established. Subsequently, a simulated atmospheric model is developed (Fig. 6), incorporating the U.S. Standard Atmosphere Model for background aerosols, urban aerosols, and dust. Using this model, we evaluate the effects of system detection SNR, Rb cell temperature fluctuations, laser frequency stability, and the omission of Mie scattering signal transmittance T_a (T_a=0) on detection errors. The Monte Carlo method is applied to establish LiDAR equations under the conditions described, enabling backscattering coefficient retrieval based on theoretical derivation. Retrieval errors are then computed to demonstrate the integrated effect. Specifically, the retrieval error of the backscattering coefficient is calculated under the conditions where the Rb cell operates at 70 ℃ with a ±1 ℃ temperature fluctuation and laser output frequency fluctuation within 100 MHz.

HSRL system measurement accuracy is highly sensitive to the SNR, especially at elevated Rb cell temperatures, which can degrade the molecule channel signal. When the Rb cell temperature exceeds 65 ℃, SNR becomes the primary factor affecting measurement results, with retrieval errors reaching up to 20%. In addition, the retrieval error of the backscattering coefficient increases with higher Rb cell temperature due to decreased Rayleigh echo transmittance (Fig. 9). If the Rb cell temperature fluctuation is within ±1 ℃ when the temperature exceeds 65 ℃, the influence on backscattering coefficient retrieval error is relatively minor (Fig. 12). Higher Rb cell temperatures can also help reduce the measurement error from temperature fluctuations. With an Rb cell temperature above 65 ℃ and Mie scattering transmittance T_a set to zero, the backscattering coefficient retrieval error remains below 1%. Moreover, higher Rb cells correlate with reduced retrieval error at higher aerosol concentrations. Finally, fluctuations in laser source frequency significantly influence retrieval results. When frequency fluctuations reach 1 GHz, retrieval errors exceed 10%, even in the absence of other error factors. By contrast, at a 70 ℃ operating temperature with a 100 MHz frequency fluctuation range, the relative retrieval error reduces to 0.1% (Fig. 14).

The operational temperature of the ⁸⁷Rb absorption cell critically influences HSRL system retrieval accuracy. With an absorption cell length of 63 mm, the recommended temperature range is 65 ℃ to 75 ℃. Within this range, system SNR, laser frequency stability, and Rb cell temperature stability are vital factors influencing detection accuracy. The simulation results demonstrate that when the ⁸⁷Rb absorption cell is 63 mm in length, operating at 70 ℃ with laser frequency stability within 100 MHz, the comprehensive retrieval deviation of the backscattering coefficient remains below 10% (Fig. 15).

As a critical technology for high spatial resolution and accurate polarization detection, the division of focal plane (DoFP) polarization detectors is achieved via the integration of micro-polarizer arrays (MPAs) with image sensors. These detectors are pivotal in numerous applications, ranging from remote sensing and biomedical imaging to military surveillance and industrial process monitoring. However, their potential is restricted by performance problems caused by crosstalk, which degrades the extinction ratio (ER) and reduces detection accuracy. Arising from both optical and electrical interactions, crosstalk negatively influences polarization measurement precision, thereby limiting the broader adoption of DoFP polarization detectors in high-performance systems. Despite their increasing importance, comprehensive analyses of crosstalk mechanisms and effective mitigation strategies remain insufficient. We aim to systematically investigate the origins and effects of crosstalk, analyze its influence on ER, and introduce an innovative solution to suppressing crosstalk by a novel optoelectronic isolation design. This approach addresses both optical and electrical crosstalks for enhancing the functionality and reliability of DoFP polarization detectors.

Key findings of our study include the following aspects. 1) Theoretical analysis. Our study starts with a systematic analysis of crosstalk mechanisms to identify contributing factors. Optical crosstalk is primarily caused by diffraction and reflection processes within the detector’s structural layers, and electrical crosstalk arising from charge diffusion across adjacent pixels is quantified and evaluated. Meanwhile, their collective effects on ER are modeled to highlight critical performance bottlenecks. 2) Numerical simulation. Simulations play a central role in validating the theoretical findings and exploring mitigation strategies. A front-side illuminated (FSI) dual-pixel DoFP detector model is developed and simulated by adopting the FDTD and CHARGE modules in Ansys Optics Launcher 2024 R1. The simulations focus on electromagnetic field distributions and charge transport phenomena, enabling accurate predictions of pixel-level crosstalk. By employing these tools, the ER is evaluated based on simulation-derived electrical parameters, which provides quantitative insights into the degradation mechanisms and tests of the proposed design concepts for mitigating crosstalk. 3) Experimental verification. MPA is directly fabricated on the photosensitive surface of a commercial CMOS image sensor (CMV4000) based on focused ion beam (FIB) technology. The detector contains 0°, 60°, and 120° polarization strips, which are composed of sub-wavelength Al gratings. Additionally, a line shape light source polarization testing system based on a cylindrical mirror is built to measure the polarization transmittance and quantify the crosstalk between pixels [Figs. 7(b) and 9].

Crosstalk mechanisms. Analysis reveals that optical crosstalk primarily originates from diffraction phenomena in the thick passivation layers and scattering by internal metallic structures, particularly the electrodes. In contrast, electrical crosstalk arises from carrier migration across pixel boundaries, significantly impairing ER. Finally, the undesired interference degrades ER. Fabrication and testing. The fabricated DoFP polarization detector demonstrates polarization transmittance curves with distinct sinusoidal characteristics. The measured crosstalk percentages for adjacent pixels reach as high as 19.26% (Table 2). Optoelectronic isolation design. To mitigate crosstalk, we propose a novel optoelectronic isolation strategy, which leverages Al conductors embedded within silicon dioxide insulators to block both photon migration and electron migration (Fig. 11). Simulations of this design demonstrate a substantial reduction in crosstalk, leading to significant ER improvements. Notably, the electrical ER reaches values that approach the intrinsic limits set by the MPA design itself (Fig. 12), thereby emphasizing the effectiveness of the proposed solution.

We systematically half-quantify the influence of crosstalk on the performance of DoFP polarization detectors and introduce a novel optoelectronic isolation strategy as an effective countermeasure. By combining theoretical modeling, numerical simulations, and experimental validation, we provide a comprehensive understanding of the mechanisms underlying optical and electrical crosstalks. The proposed design successfully suppresses both forms of interference, leading to significant enhancements in ER and overall detector performance. These findings provide both a valuable foundation for the design and development of high-performance DoFP polarization detectors and a reference for the integration of other array metasurfaces and planar array detectors. In the future, more complex detector models, real structures, and doping parameters will be adopted to quantitatively study the influence of photoelectric isolation.