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Abstract This paper investigates the use of artificial neural networks (ANNs) as a viable digital twin or alternative to the typical whispering gallery mode (WGM) optical sensors in engineering systems, especially in dynamic environments like robotics. Because of its fragility and limited endurance, the WGM sensor which is based on micro-optical resonators is inappropriate in these kinds of situations. In order to address these issues, the paper suggests an ANN that is specifically designed for the system and makes use of the WGM sensor’s high-quality factor (Q-factor). By extending the applicability and endurance to dynamic contexts and reducing fragility problems, the ANN seeks to give high-resolution measurement. In order to minimize post-processing requirements and maintain system robustness, the study goal is for the ANN to function as a representative predictor of the WGM sensor output. The GUCnoid 1.0 humanoid robot is used in the paper as an example to show how the WGM optical sensors may improve humanoid robot performance for a variety of applications. The results of the experiments demonstrate that the sensitivity, precision, and resolution of ANN outputs and actual WGM shifts are equivalent. As a consequence, current obstacles to the widespread use of high-precision sensing in the robotics industry are removed, and the potential of ANNs as virtual substitutes or the digital twin for genuine WGM sensors in robotics systems is validated. So, this paper can be very beneficial not only to the sensing technologies that are used in robotics, which are subjected to the dynamic environments, but also to the industrial automation and human-machine interface.

Abstract Random matrices are fundamental in photonic computing because of their ability to model and enhance complex light interactions and signal processing capabilities. In manipulating classical light, random operations are utilized for random projections and dimensionality reduction, which are important for analog signal processing, computing, and imaging. In quantum information processing, random unitary operations are essential to boson sampling algorithms for multiphoton states in linear photonic circuits. Random operations are typically realized in photonic circuits through fixed disordered structures or through large meshes of interferometers with reconfigurable phase shifters, requiring a large number of active components. In this article, we introduce a compact photonic circuit for generating random matrices by utilizing programmable phase modulation layers interlaced with a fixed mixing operator. We show that using only two random phase layers is sufficient for producing output optical signals with a white-noise profile, even for highly sparse input optical signals. We experimentally demonstrate these results using a silicon-based photonic circuit with tunable thermal phase shifters and waveguide lattices as mixing layers. The proposed circuit offers a practical method for generating random matrices for photonic information processing and for applications in data encryption.

Abstract The global surge in opioid misuse, particularly fentanyl, presents a formidable public health challenge, highlighted by increasing drug-related mortalities. Our study introduces a novel approach for on-site quantitative detection of fentanyl in heroin, employing machine learning-enabled surface-enhanced Raman spectroscopy (SERS) on superabsorbing metasurfaces. The metasurface enables superior light absorption (>90%) across a broad wavelength range (580–1100 nm). This architecture facilitates significant electromagnetic field enhancement, over 2.19 × 107, ensuring high sensitivity, uniformity, and reproducibility. Our method precisely captured SERS signals across a detection range of 1–100 μg/mL in fentanyl solutions, fentanyl-heroin mixtures, and fentanyl-spiked saliva, demonstrating its versatility and practical utility. Incorporation of partial least squares regression into our analysis achieved over 93% accuracy in concentration predictions, eliminating the need for pre-data processing or specialized personnel. This marks a key advancement in rapid, accurate fentanyl detection, aiding the fight against the opioid crisis and improving public health safety.

Abstract Photodetectors equipped with multi-parameter control hold the potential to deliver exceptional performance in a wide range of scenarios, paving the way for developing novel spin-opto-electronic devices. Nevertheless, the integration of such capabilities within a single device is challenging due to the necessity of harmonizing multiple materials with varying degrees of freedom. In this study, we introduce the van der Waals magnet CrSBr, featuring inherent anisotropy and distinctive spin-electronic coupling, to this realm. The linear dichroic ratio of the photocurrent in CrSBr tunneling device can reach ~60 at 1.65 K, and the photoresponse experiences a significant boost with increasing magnetic field. Additionally, the unique spin-charge coupling engenders a photon energy-dependent photocurrent that is modulated by an external field and is validated by first-principle calculations. Our findings elucidate the effective multi-parameter control of photodetection based on vdWs magnet CrSBr, highlighting its potential applications in cutting-edge optoelectronic devices and as a highly sensitive probe medium.

Abstract Neuromorphic vision hardware, embedded with multiple functions, has recently emerged as a potent platform for machine vision. To realize memory in sensor functions, reconfigurable and non-volatile manipulation of photocarriers is highly desirable. However, previous technologies bear mechanism challenges, such as the ambiguous optoelectronic memory mechanism and high potential barrier, resulting in a limited response speed and a high operating voltage. Here, for the first time, we propose a critical band-to-band tunnelling (BTBT) based device that combines sensing, integration and memory functions. The nearly infinitesimal barrier facilitates the tunnelling process, resulting in a broadband application range (940 nm). Furthermore, the observation of dual negative differential resistance (NDR) points confirms that the critical BTBT of photocarriers contributes to the sub-microsecond photomemory speed. Since the photomemory speed, with no motion blur, is important for motion detection, the critical BTBT memory is expected to enable moving target tracking and recognition, underscoring its superiority in intelligent perception.

Abstract As a frontier technology, holography has important research values in fields such as bio-micrographic imaging, light field modulation and data storage. However, the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough, which has seriously hindered the development of holography. Here, a novel holographic camera is proposed to solve the above inherent problems completely. The proposed holographic camera consists of the acquisition end and the calculation end. At the acquisition end of the holographic camera, specially configured liquid materials and liquid lens structure based on voice-coil motor-driving are used to produce the liquid camera, so that the liquid camera can quickly capture the focus stack of the real 3D scene within 15 ms. At the calculation end, a new structured focus stack network (FS-Net) is designed for hologram calculation. After training the FS-Net with the focus stack renderer and learnable Zernike phase, it enables hologram calculation within 13 ms. As the first device to achieve real-time incoherent acquisition and high-fidelity holographic reconstruction of a real 3D scene, our proposed holographic camera breaks technical bottlenecks of difficulty in acquiring the real 3D scene, low quality of the holographic reconstructed image, and incorrect defocus blur. The experimental results demonstrate the effectiveness of our holographic camera in the acquisition of focal plane information and hologram calculation of the real 3D scene. The proposed holographic camera opens up a new way for the application of holography in fields such as 3D display, light field modulation, and 3D measurement.

Abstract Polarization is an important attribute of light and can be artificially modulated as a versatile information carrier. Conventional polarization-sensitive photodetection relies on a combination of polarizing optical elements and standard photodetectors, which requires a substantial amount of space and manufacturing expenses. Although on-chip polarized photodetectors have been realized in recent years based on two-dimensional (2D) materials with low-symmetry crystal structures, they are limited by the intrinsic anisotropic property and thus the optional range of materials, the operation wavelength, and more importantly, the low anisotropic ratio, hindering their practical applications. In this work, we construct a versatile platform that transcends the constraints of material anisotropy, by integrating WSe2-based photodetector with MoS2-based field-effect transistor, delivering high-performance broadband polarization detection capability with orders of magnitude improvement in anisotropic ratio and on/off ratio. The polarization arises from hot electron injection caused by the plasmonic metal electrode and is amplified by the transistor to raise the anisotropic ratio from 2 to an impressive value over 60 in the infrared (IR) band, reaching the level of existing applications. Meanwhile, the system achieves a significant improvement in photosensitivity, with an on/off ratio of over 103 in the IR band. Based on the above performance optimization, we demonstrated its polarization-modulated IR optical communication ability and polarized artificial vision applications with a high image recognition accuracy of ~99%. The proposed platform provides a promising route for the development of the long-sought minimized, high-performance, multifunctional optoelectronic systems.

Abstract Local microcurrent monitoring is of great significance for biological and battery systems, yet it poses a formidable challenge. The current measurement techniques rely on electromagnetic materials which inevitably introduce interference to the system under examination. To address this issue, a promising approach based on a dielectric fiber-optic sensor is demonstrated. The microfiber is capable of detecting microcurrent through monitoring the localized proton concentration signal with a pH resolution of 0.0052 pH units. By sensing the refractive index variation surrounding the sensor induced by the interaction between local proton concentration changes and oxidizer-treated microfiber surface through the evanescent field, this sensing mechanism effectively avoids the interference of the electromagnetic material on the performance of the tested system. This sensor exhibits a limit of detection for microcurrent of 1 μA. The sensing region is a microfiber with a diameter of 8.8 μm. It can get invaluable information that cannot be obtained through conventional electrochemical methods. Examples include photocurrent attenuation in photogenerated carrier materials during illumination, electrical activation in nerve cells, and fluctuations in the efficiency of electrical energy generation during battery discharge. This approach provides a powerful complement to electrochemical methods for the elucidation of microscale reaction mechanisms. The information provided by the prepared dielectric fiber-optic sensor will shed more light on proton kinetics and electrochemical and electrobiological mechanisms, which may fill an important gap in the current bioelectricity and battery monitoring methods.

Abstract To reveal the fundamental aspects hidden behind a variety of transient events in mechanics, physics, and biology, the highly desired ability to acquire three-dimensional (3D) images with ultrafast temporal resolution has been long sought. As one of the most commonly employed 3D sensing techniques, fringe projection profilometry (FPP) reconstructs the depth of a scene from stereo images taken with sequentially structured illuminations. However, the imaging speed of current FPP methods is generally capped at several kHz, which is limited by the projector-camera hardware and the number of fringe patterns required for phase retrieval and unwrapping. Here we report a novel learning-based ultrafast 3D imaging technique, termed single-shot super-resolved FPP (SSSR-FPP), which enables ultrafast 3D imaging at 100,000 Hz. SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while “regenerating” the lost spatial resolution through deep learning. To demonstrate the high spatio-temporal resolution of SSSR-FPP, we present 3D videography of several transient scenes, including rotating turbofan blades, exploding building blocks, and the reciprocating motion of a steam engine, etc., which were previously challenging or even impossible to capture with conventional methods. Experimental results establish SSSR-FPP as a significant step forward in the field of 3D optical sensing, offering new insights into a broad spectrum of dynamic processes across various scientific disciplines.

Abstract Harmonic mode-locking, realized actively or passively, is an effective technique for increasing the repetition rate of ultrafast lasers. It is critically important to understand how a harmonically mode-locked pulse train responds to external perturbations and noise, so as to make sure that it is stable and resistant to noise. Here, in a series of carefully designed experiments, we elucidate the retiming dynamics of laser pulses generated in a soliton fiber laser harmonically mode-locked at GHz frequencies to the acoustic resonance in a photonic crystal fiber (PCF) core. We characterize the self-driven optomechanical lattice, which is distributed along the PCF and provides the structure that supports harmonic mode-locking, using a homodyne setup. We reveal that, after an abrupt perturbation, each soliton in the lattice undergoes damped oscillatory retiming within its trapping potential, while the retiming is strongly coupled to soliton dissipation. In addition, we show, through statistical analysis of the intra-cavity pulse spacing, how the trapping potentials are effective for suppressing timing jitter. The measurements and the theory developed in this work lay the groundwork for studies of the general stability and noise performance of harmonically mode-locked lasers as well as providing valuable insight into generic multi-pulse phenomena in mode-locked lasers.

Abstract Cavity electrodynamics offers a unique avenue for tailoring ground-state material properties, excited-state engineering, and versatile control of quantum matter. Merging these concepts with high-field physics in the terahertz (THz) spectral range opens the door to explore low-energy, field-driven cavity electrodynamics, emerging from fundamental resonances or order parameters. Despite this demand, leveraging the full potential of field-driven material control in cavities is hindered by the lack of direct access to the intra-cavity fields. Here, we demonstrate a new concept of active cavities, consisting of electro-optic Fabry-Pérot resonators, which measure their intra-cavity electric fields on sub-cycle timescales. We thereby demonstrate quantitative retrieval of the cavity modes in amplitude and phase, over a broad THz frequency range. To enable simultaneous intra-cavity sampling alongside excited-state material control, we design a tunable multi-layer cavity, enabling deterministic design of hybrid cavities for polaritonic systems. Our theoretical models reveal the origin of the avoided crossings embedded in the intricate mode dispersion, and will enable fully-switchable polaritonic effects within arbitrary materials hosted by the hybrid cavity. Electro-optic cavities (EOCs) will therefore serve as integrated probes of light-matter interactions across all coupling regimes, laying the foundation for field-resolved intra-cavity quantum electrodynamics.

Abstract Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial attention. However, broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic circuitry. Here, we report an analog parallel processor (APP) based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog domain. The APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel sequences. Via photonic analog parallelism, data rate and data volume in each sequence are simultaneously compressed, which mitigates the requirement on each parallel computing core. Moreover, the fusion of the outputs from each computing core is equivalent to directly processing broadband signals. In the proof-of-concept experiment, two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated circuit. The bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is achieved. The wireless communication rate of 8 Gbit/s is also validated. Breaking the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture, we anticipate that the proposed APP will accelerate the development of powerful opto-electronic processors as critical support for applications such as satellite networks and intelligent driving.

Cryophotonics is a promising way of boosting state-of-the-art photonic components using cryogenic temperatures. In this work, in addition to confirm our experimental results with measurements on another component, we present a theoretical analysis of SOA behavior at cryogenic temperatures. Based on the obtained experimental results, we expand the SOA model range by introducing temperature dependence on the main SOA physical parameters such as band gap energy level, recombination coefficients, internal losses and effective electron and holes masses. The model is applicable over a wide temperature range from ambient down to cryogenic temperatures. A qualitative agreement is found between simulations and experiments. The comparisons are given down to 70 K in terms of gain spectrum, saturation output power and noise figure which demonstrate the effectiveness of the model.

Free-space optics (FSO) communication systems can be applied to cellular mobile communication networks to offer high-data-rate services to mobile users. In such networks, the exposure of optical receivers to direct sunlight could degrade the system performance and damage light-sensitive photo-detectors permanently. We investigate the average probability of the exposure of optical receiver to direct sunlight in FSO-based wireless mobile communication networks. We first estimate the probability of exposure to direct sunlight in open areas and then extend the theoretical analysis to the urban areas where the direct sunlight can be blocked by buildings. The validity of the analyses is evaluated through Monte Carlo simulations over a virtual city. The results show that the probability of exposure to direct sunlight ranges from 10−6 to 10−2, depending upon the receiver's field-of-view (FoV), latitude, average building heights, and the height of base stations (BSs). We also find out that the probabilities increase with the receiver's FoV to the power of 1.8 when the receiver's FoV is larger than the angular diameter of the sun. In urban areas, the probability decays exponentially with the average height of buildings exceeding the half of the BS height. The results also show that the probability rises with the BS height to the power of ∼2.6 under the same conditions. Finally, we estimate the outage time of FSO-based wireless mobile communications occurring by the exposure to direct sunlight. When the receiver's FoVs are 1 and 10 degrees, the outage times are estimated to be

The dual-comb spectrometer has emerged as a preferred instrument in multiheterodyne spectroscopy due to its high-resolution and fast-scanning capabilities. Conventional dual-comb spectroscopy (DCS) requires locking or reference systems, increasing both system complexity and cost, and limiting its commercial feasibility. Self-correction offers a promising alternative, though it demands a high refresh rate for interferograms (IGMs), corresponding to the repetition frequency difference, to counter system noise. This study examined the practical effects of self-correction at various refresh rates by measuring $\rm H^{13}C^{14}N$ absorption. The results indicate that higher repetition frequency differences improve self-correction performance, while lower repetition frequencies cause deviations in absorption line width and intensity. Practical applications must balance measurement bandwidth, which is constrained by the repetition frequency difference.

We demonstrate Mach-Zehnder interferometer-based (MZI) athermal photonic devices using the structural compensation method. Unlike previous structural compensation studies that were applied on the thermal sensitive materials such as silicon, this work is implemented in tantalum pentoxide (Ta2O5) platform whose thermo-optic coefficient is low. This allows us to achieve ultra-athermalized filters by combining the structural compensation method and the material's own thermo-optic properties. Two types of devices are proposed: the asymmetric Mach-Zehnder interferometer (AMZI) and the ring-coupled Mach-Zehnder interferometer (RMZI). The temperature-dependent wavelength shift (TDWS) of the AMZI device is only 1.98 pm/K around 1550 nm which is 4.6 times smaller than a regular MZI. The TDWS remains below 2.23 pm/K across a broad bandwidth from 1480 nm to 1580 nm. By breaking the linear dependence between the wavelength shift and the temperature change, the maximum resonance drift can be restricted by using a ring-coupled MZI. Owning to Fano effect, the transmission spectrum of the RMZI device exhibits an oscillating behavior when facing temperature changes. This work proves the effectiveness of structural compensation method on an already low thermo-optic photonic platform, paving the way towards realization of ultra-athermal integrated optical filters in a low-loss and CMOS-compatible platform.

Multiband operation is a key aspect of emerging 5G-Advanced cellular systems, also named 5.5G, which target seamless provision of multi-gigabit per second connectivity employing sub-6 GHz and mm-wave overlapping coverage. All-optical frequency conversion gives flexibility due to the feasible high-speed reconfiguration in broad bands and large radio signal bandwidth. Optical frequency combs, featuring a spectrum of discrete, equally spaced coherent frequency lines, are crucial for high-precision metrology, spectroscopy, and telecommunications. Their effectiveness in all-optical frequency conversion depends on their stability in terms of frequency drift, phase noise, and power distribution across the comb lines. This paper evaluates experimentally the generation of optical frequency combs employing two distinct technologies: a dual-driven Mach-Zehnder modulator (DD-MZM) and an optomechanical crystal cavity (OMCC), and experimentally compares their performance for all-optical frequency conversion of 5G data streams. The DD-MZM implementation generates a comb with flexible line spacing and comprising several spectrum-flat lines with low phase noise ($-88.5$ dBc/Hz at 1 kHz offset and $-108.3$ dBc/Hz at 100 kHz offset). The OMCC implementation provides a reduced footprint (182 $\mu \mathrm{m}^{2}$) since it is implemented on a silicon chip and has the extra advantage of generating an optical comb without an external local oscillator, which reduces its power requirements (under 1 mW) while providing a phase noise of $-38.3$ dBc/Hz at 1 kHz offset and $-97.1$ dBc/Hz at 100 kHz offset. The polarization stability and jitter of both implementations are also evaluated. The experimental demonstration evaluates the error vector magnitude (EVM) of frequency-converted 3GPP 5G NR signals using both implementations, confirming the successful transmission with EVM smaller than 12.01% for DD-MZM up to the third harmonic and EVM smaller than 17.36% with the OMCC first harmonic.

To address the issues in polishing medium and large aperture optical components, which are reliant on the personal experience of processing personnel and exhibit poor controllability, this study conducted a comprehensive examination of the polishing process for these planar optical components. This article constructs a processing prediction model for large-diameter ring pendulum double-sided polishing equipment. Based on this, a simulation of the theoretical removal amount distribution of ring-pendulum double sided polishing was conducted to ascertain the relationship between various process parameters and their impact on the surface removal amount distribution of the component. Based on the obtained relationship, a decision support system for the process parameters of ring-pendulum double sided polishing was developed, which achieved the deterministic removal of rings in medium and large aperture planar optical components and reduced the dependence of ring pendulum double-sided polishing on the personal experience of process decision-makers. After experimental verification, the characteristics of the simulation results of the processing prediction model were consistent with the actual processing results. The developed process parameter decision assistance system was used to make process parameter decisions for two 430 * 430 mm fused quartz components, achieving the removal of the target ring of the components. The surface figure accuracy of the two components has been processed from 3.09λ and 3.5λ to 1.53λ and 1.79λ, respectively.

Compared to intensity modulation with direct detection, coherent detection offers superior receiver sensitivity, higher spectral efficiency, and better background noise suppression. However, research on coherent detection in underwater wireless optical communication (UWOC) systems is relatively limited. This paper investigates the average bit error rate (BER) performance of coherent UWOC systems employing different phase-shift keying schemes in the presence of phase errors. The underwater turbulent channel is characterized by the mixture exponential-generalized gamma distribution, taking into account the impact of pointing errors as well. Phase errors are modeled by a Gaussian distribution. Specifically, we first derive the average BER expressions for $M$-ary phase-shift keying (MPSK) under ideal carrier phase estimation. Then, we derive the exact average BER expressions for binary phase-shift keying and quadrature phase-shift keying, as well as tight approximations for MPSK, considering the influence of phase errors. Additionally, we provide asymptotic BER expressions in the high signal-to-noise ratio region and the BER floor expression. Finally, these expressions are validated through Monte Carlo simulations.

Ultrashort optical pulse sources are often complex and resource-intensive, and thus, delivering pulses to multiple satellite locations over a fiber-optic network dynamically from a central location reduces overhead and increases the efficacy of the source usage. For robust delivery, accurate characterization of the pulse dispersion at the satellite locations without specialized equipment and its compensation is essential. This work demonstrates the delivery of dispersion-compensated pulses over standard optical fiber links and their characterization using a simple measurement module at satellite locations. The module uses a second-harmonic crystal and power detectors at the fundamental and second-harmonic wavelengths. A centrally located pulse shaper-based interferometer creates pulse pairs with varying time delays during the characterization phase. Together with the remote detectors, this provides the field and intensity autocorrelations, which describe the spectral and temporal domain of the pulse. We demonstrate our technique by transmitting dispersion compensated sub-400 fs pulses over two fiber optic links of 50 and 100 meters. The pulses are shaped adaptively before transmission to compensate for the dispersion-induced distortions. The pulse intensity and power spectrum are measured remotely and agree with those made at the source. This provides an easy distribution method for femtosecond lasers from central to satellite locations via standard optical fiber links on a time-share basis and their remote characterization.

We propose and experimentally demonstrate a terahertz (THz) source based on optical rectification in lithium niobate wafers driven by an ultrafast ytterbium laser. Terahertz pulses with a repetition of 10 kHz and a spectrum of 0.1∼6 THz are generated via a collinear geometry. The phase-matching condition is analyzed and the effect of crystal thickness is investigated. Our study demonstrates a compact and stable source for terahertz spectroscopy and imaging applications.

Abstract In this paper, a graphene oxide (GO) composite film-coated humidity sensor is proposed based on the hollow core fiber (HCF). A segment of the HCF is spliced between two segments of the single-mode fiber (SMF). The relative humidity (RH) sensing characteristics of the sensor are experimentally investigated by observing the intensity shift of resonant dips in the transmission spectrum, which shows the GO composite film-coated HCF has the good stability in the measurement of humidity. The maximum humidity sensitivity of 0.12 dB/%RH is obtained in the RH range of 30%–78%. The proposed sensor has the advantages of the simple structure, easy fabrication, good stability, and high performance, which can be applied to marine climate detection, tunnel air humidity detection, agricultural testing, and other fields.

Abstract This study presents the two-dimensional (2D) image of a subsurface structure reconstructed using an imaging method based on the photothermal effect. The photothermal imaging method is based on the deflection method using two lasers: pump and probe lasers. A continuous scanning technique is proposed for 2D (x- and y-directions) surface scanning. The continuous scanning method is compared with the conventional point-by-point scanning technique, and a low-pass fast Fourier transform filter and a Marr-Hildreth detector are found to produce significant results. The photothermal imaging method with continuous 2D surface scanning is performed on three copper-resin double-layer samples with different subsurface structures. The subsurface structures of the copper-resin double-layer samples comprise a square block of 5×5 mm2 area and blocks shaped as the alphabet letters “T” and “F”. The letters are 3 mm wide and 10×13 mm2 in area. All three shapes are 1 mm thick and located at a depth of 0.5 mm from the surface of the copper block. The reconstructed photothermal images show an absolute error within 0.122 mm compared with the actual subsurface structure, equivalent to a 2.3% relative error.

Abstract Since photo-induced polymerization of the ultra-violet (UV)-curing adhesive from a fluid state to a solid state is an exothermic process, the UV curing exothermic behavior can be regarded as a potential evaluation methodology to analyze UV-curing kinetics. Herein, a fiber Bragg grating (FBG)-based UV curing exothermic behavior monitoring is proposed to evaluate the UV-curing dynamic process and analyze a series of thermal and mechanical properties changes during curing. The exothermic behavior of the UV curing adhesive during curing and the feasibility of FBG-based curing kinetic analysis scheme are verified experimentally, full cycle cure monitoring of the UV curing adhesive can be realized by this FBG-based curing kinetic analysis scheme, and the UV-curing kinetics of four different types of the UV curing adhesive are corresponding to different exothermic behaviors. Compared with curing process evaluation based on refractive index variation, this FBG-based exothermic behavior monitoring has the ability to extract more details of the curing process, and some curing stages with negligible refractive index changes also can be distinguished. By using this proposed scheme, the UV-curing dynamic process and multiple characteristic parameters, such as curing time, time constant, transient temperature rise, and residual stress, can be evaluated, which may contribute to evaluating and analyzing UV-curing kinetics more comprehensively.

Abstract A novel optical fiber hydrogen sensor based on the π-phase-shifted grating and partial coated Pd/Hf composite film is proposed and experimentally demonstrated in this paper. The hydrogen sensitive Pd/Hf film with the length of 4 mm is successfully deposited in the π-phase-shifted grating region by the magnetron sputtering process and rotating fixture technology. Since the hydrogen sensitivity between the notch and flank wavelengths of the π-phase-shifted grating is different due to the partial coating only on the π-phase-shifted grating region, the relative shift between the notch and flank wavelengths is employed to characterize the hydrogen concentration in this paper. The hydrogen calibration results show that the sensor shows the good response and repeatability. At the temperature of 20 °C and the hydrogen concentration of 2%, the wavelength distance shifts of 200 nm and 500 nm Pd/Hf coatings are 12.6 pm and 33.5 pm, respectively.

Abstract Color center platforms have been at the forefront of quantum nanophotonics for applications in quantum networking, computing, and sensing. However, large-scale deployment of this technology has been stifled by a lack of ability to integrate photonic devices at scale while maintaining the properties of quantum emitters. We address this challenge in silicon carbide, which has both commercially available wafer-scale substrates and is a host to color centers with desirable optical and spin properties. Using ion beam etching at an angle, we develop a 5-inch wafer process for the fabrication of triangular cross-section photonic devices in bulk 4H-SiC. The developed process has a variability in etch rate and etch angle of 5.4% and 2.9%, respectively. Furthermore, the integrated color centers maintain their optical properties after the etch, thus achieving the nanofabrication goal of wafer-scale nanofabrication in quantum-grade silicon carbide.

Abstract All-dielectric nonlinear metasurfaces have higher damage thresholds and more flexible manipulation of resonant modes. Because of the intrinsic advantages of short-wavelength regions, related research gradually moved from the visible to the vacuum ultraviolet (VUV) region. This review introduces the theoretical framework: resonant modes enhance the local electromagnetic field and promote nonlinear polarization. After analyzing nonlinear efficiency enhancement mechanisms, we explore nonlinear modulation strategies. Finally, potential research directions are discussed.

Abstract Metal-halide perovskite nanowire array photodetectors based on the solution method are valuable in the field of polarized light detection because of their unique one-dimensional array structure and excellent photoelectric performance. However, the limited wettability of liquids poses challenges for achieving large-scale and high-quality perovskite nanowire arrays. To address this issue, we develop a facile method utilizing capillary condensation to grow high-quality centimeter-scale perovskite nanowire arrays. Based on these nanowires, the fabricated photodetector showcases specific detectivities of 1.95 × 1013 jones, surpassing commercially available silicon detectors in weak-light detection capabilities. The weak-light imaging capability of our nanowire photodetectors has been successfully demonstrated at intensities below 54 nW/cm2. Moreover, the nanowire arrays also display excellent polarization absorption characteristics, promising applications in polarized light detection. Notably, the perovskite nanowire arrays can be grown on flexible substrates by employing capillary condensation, which retains 83% of their properties after 2000 bending cycles. This research enhances the potential of perovskite nanowire arrays photodetector in practical applications.

Abstract We propose and validate a novel optical semantic transmission scheme using multimode fiber (MMF). By leveraging the frequency sensitivity of intermodal dispersion in MMFs, we achieve high-dimensional semantic encoding and decoding in the frequency domain. Our system maps symbols to 128 distinct frequencies spaced at 600 kHz intervals, demonstrating a seven-fold increase in capacity compared to conventional communication encoding. We further enhance spectral efficiency by implementing 4-level pulse amplitude modulation (PAM-4), achieving 9.12 bits/s/Hz without decoding errors. Additionally, we explore the application of this system for sentiment analysis using the IMDb movie review dataset. By encoding semantically similar symbols to adjacent frequencies, the system’s noise tolerance is effectively improved, facilitating accurate sentiment analysis. This work highlights the potential of MMF-based semantic communication to enhance both capacity and robustness in optical communication systems, offering promising applications in bandwidth-constrained and noisy environments.

Abstract A stacked metamaterial MEMS (meta-MEMS) chip is proposed, which can perfectly absorb electromagnetic waves, convert them into mechanical energy, drive movement of the optical micro-reflectors array, and detect millimeter waves. It is equivalent to using visible light to image a millimeter wave. The meta-MEMS adopts the design of upper and lower chip separation and then stacking to achieve the “dielectric-resonant-air-ground” structure, reduce the thickness of the metamaterial and MEMS structures, and improve the performance of millimeter wave imaging. For verification, we designed and prepared a 94 GHz meta-MEMS focal plane array chip, in which the sum of the thickness of the metamaterial and MEMS structures is only 1/2500 wavelength, the pixel size is less than 1/3 wavelength, but the absorption rate is as high as 99.8%. Moreover, a light readout module was constructed to test the millimeter wave imaging performance. The results show that the response speed can reach 144 Hz and the lens-less imaging resolution is 1.5 mm.

Abstract A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features. Here, we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining (DS) model. This enhanced imaging modality integrates high-throughput 3D imaging, low sample variability and high interpretability, making it suitable for 3D histology studies. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples, achieving consistent staining quality and enhancing contrast across cortical layer boundaries. Additionally, we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks, allowing for visualization of meso-scale vessel networks in the white matter. We believe that our technique has the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.

Abstract Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization.

Abstract Accurately and swiftly characterizing the state of polarization (SoP) of complex structured light is crucial in the realms of classical and quantum optics. Conventional strategies for detecting SoP, which typically involves a sequence of cascaded optical elements, are bulky, complex, and run counter to miniaturization and integration. While metasurface-enabled polarimetry has emerged to overcome these limitations, its functionality predominantly remains confined to identifying SoP within the standard Poincaré sphere framework. The comprehensive detection of SoP on the higher-order Poincaré sphere (HOPS), however, continues to be a huge challenge. Here, we propose a general polarization metrology method capable of fully detecting SoP on any HOPS through a single measurement. The underlying mechanism relies on transforming the optical singularities and Stokes parameters into visualized intensity patterns, facilitating the extraction of all parameters that fully determine a SoP. We actualize this concept through a novel meta-device known as the metasurface photonics polarization clock, which offers an intuitive display of SoP using four distinct pointers. As a proof of concept, we theoretically and experimentally demonstrate fully resolving SoPs on the 0th, 1st, and 2nd HOPSs. Our implementation opens up a new pathway towards real-time polarimetry of arbitrary beams featuring miniaturized size, a simple detection process, and a direct readout mechanism, promising significant advancements in fields reliant on polarization.

Abstract The size of InGaN micro-LEDs is continuously decreasing to meet the demands of various emerging applications, especially in tiny micro-displays such as AR/VR. However, the conventional pixel definition based on plasma etching significantly damages the mesa sidewalls, leading to a severe reduction in efficiency as the micro-LED size decreases. This seriously impedes the development and application of micro-LEDs. In this work, we comprehensively explain the origin of micro-LED sidewall effects and corresponding physical models. Subsequently, we systematically review recent progress in micro-LED fabrication aiming at suppressing sidewall effects. Furthermore, we discuss advancements in micro-LED fabrication with “damage-free” techniques, which hold the potential to fundamentally address the issue of plasma damage in the micro-LED process. We believe this review will deepen the understanding of micro-LED sidewall effects and provide a better insight into the latest associated fabrication technologies for high-efficient InGaN micro-LEDs.

Abstract Transparent organic light-emitting diode (TrOLED) displays represent cutting-edge technology posed to significantly enhance user experience. This study addresses two pivotal challenges in TrOLED development. Firstly, we focus on the innovation of transparent cathodes, a fundamental component in TrOLEDs, by introducing a ZnO/Yb:Ag cathode. This cathode employs a combination of seed layer and metal doping techniques to achieve a highly uniform surface morphology and a low surface energy barrier. The optimized Yb:Ag cathode on ZnO, with a mere thickness of 15 nm, exhibits remarkable properties: an extremely low surface roughness of 0.52 nm, sheet resistance of 11.6 Ω ϒ−1, an optical transmittance of 86.7% at 510 nm, and tunable work function (here, optimized to be 3.86 eV), ensuring superior electron injection capability. Secondly, we propose a novel TrOLED pixel structure that features selective bidirectional viewing, allowing different types of information to be selectively displayed on each side while preserving overall transparency and minimizing pixel complexity. This design innovation distinguishes itself from conventional TrOLEDs that display images on only one side. The bidirectional TrOLED design not only enhances openness and esthetic appeal but also holds promise for diverse applications across various user environments.

Accurate dense reconstruction of unknown spatial environments is crucial for applications such as underground exploration and planetary missions. Existing methods face challenges like observing blind spots and the difficulty of edge feature extraction in point clouds with non-repetitive scanning LiDARs. This paper first uses a novel odometry and mapping system integrating two solid-state LiDARs and an IMU to obtain distortion-compensated point clouds and corresponding poses, which are utilized to generate submaps. Our approach then leverages these accumulated submaps to efficiently extract edge features. Experimental results demonstrate that our submap-based method effectively identifies edge features within point clouds, which can be used for association with panoramas for joint optimization in the future.

The human brain can efficiently handle multiple tasks simultaneously, with the structural segregation of brain regions closely linked to their functional specialization. In this paper, we propose and experimentally demonstrate multifunctional photonic reservoir computing (MPRC), inspired by this biological characteristic. The neuron states of the photonic reservoir are divided into distinct sections for different tasks by the designing of input matrix, and only a set of output weight matrix, applicable to all tasks, is ultimately trained. Therefore, MPRC is capable of handling multiple tasks simultaneously with a fixed set of parameters, mitigating the tedious process of hyperparameter optimization. The influence of neuron partition size and operating parameters on MPRC performance is studied systematically. Furthermore, the generalizability of MPRC is validated by performing four different tasks simultaneously. Finally, our results are experimentally demonstrated using semiconductor lasers with a time-delay feedback loop.

The burgeoning advancements in Light Emitting Diode (LED) technology have opened avenues for applications beyond traditional display and lighting, notably in the realm of 3D printing. This study introduces a Local Dimming (LD) method that employs LED backlight technology to enhance the edge quality of Liquid Crystal Display (LCD) photocuring 3D printing (LPP). LPP, known for its rapid and cost-effective fabrication, faces challenges with light leakage and precision under high-power ultraviolet (UV) light. By locally adjusting the backlight array's brightness to conform to the digital mask's shape, this study effectively suppresses LCD light leakage and improves edge definition. A comprehensive investigation into the LD method's process parameters—exposure time, pixel compensation coefficient, and initial backlight calculation—reveals significant improvements in print quality. The LD method achieves up to an 81.56% enhancement in dimensional accuracy compared to the conventional full-backlight technique. This research not only addresses the intrinsic light leakage in LCDs but also facilitates the high-resolution printing of complex structures, thus expanding the utility of LED devices in additive manufacturing.

Abstract We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system’s performance was evaluated using diverse datasets, including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios, the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition, this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition, the system achieved a classification accuracy of about 68% for the ten-class subset, surpassing the performance of conventional benchmark, the Hidden Markov Model with Gaussian Mixture Model.

Abstract Multifunctional materials have attracted tremendous attention in intelligent and interactive devices. However, achieving multi-dimensional sensing capabilities with the same perovskite quantum dot (PQD) material is still in its infancy, with some considering it currently challenging and even unattainable. Drawing inspiration from neurons, a novel multifunctional CsPbBr3/PDMS nanosphere is devised to sense humidity, temperature, and pressure simultaneously with unique interactive responses. The carefully engineered polydimethylsiloxane (PDMS) shell enables the reversible activity of the core CsPbBr3, serving a dual role similar to dendrites in conveying and evaluating external stimuli with high sensitivity. Molecular dynamics analysis reveals that the PDMS shell with proper pore density enhances the conductivity in water and heat, imparting CsPbBr3 with sensitive but reversible properties. By tailoring the crosslinking density of the PDMS shell, nanospheres can surprisingly show customized sensitivity and reversible responses to different level of stimuli, achieving over 95% accuracy in multi-dimensional and wide-range sensing. The regular pressure-sensitive property, discovered for the first time, is attributed to the regular morphology of the nanosphere, the inherent low rigidity of the PDMS shell, and the uniform distribution of the CsPbBr3 core material in combination. This study breaks away from conventional design paradigms of perovskite core-shell materials by customizing the cross-linked density of the shell material. The reversible response mechanism of nanospheres with gradient shell density is deeply explored in response to environmental stimuli, which offers fresh insights into multi-dimensional sensing and interactive display applications.

Abstract In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe2 homojunction. This approach overcomes traditional limitations, such as constrained Fermi level tunability, persistent dark current, and limited photoresponse dimensionality through dynamic energy band modulation driven by palladium (Pd) ion migration. The critical role of Pd ion migration is thoroughly supported by first-principles calculations, numerical simulations, and experimental verification, demonstrating its effectiveness in enhancing device performance. Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. This combination enables our spectrometer to achieve an exceptional peak wavelength accuracy of 0.18 nm and a spectral resolution of 2 nm within the 630–640 nm range. This development marks a significant advancement in the creation of compact, high-efficiency spectroscopic instruments and offers a versatile platform for applications across diverse material systems.

Abstract Light manipulation and control are essential in various contemporary technologies, and as these technologies evolve, the demand for miniaturized optical components increases. Planar-lens technologies, such as metasurfaces and diffractive optical elements, have gained attention in recent years for their potential to dramatically reduce the thickness of traditional refractive optical systems. However, their fabrication, particularly for visible wavelengths, involves complex and costly processes, such as high-resolution lithography and dry-etching, which has limited their availability. In this study, we present a simplified method for fabricating visible Fresnel zone plate (FZP) planar lenses, a type of diffractive optical element, using an i-line stepper and a special photoresist (color resist) that only necessitates coating, exposure, and development, eliminating the need for etching or other post-processing steps. We fabricated visible FZP lens patterns using conventional photolithography equipment on 8-inch silica glass wafers, and demonstrated focusing of 550 nm light to a diameter of 1.1 μm with a focusing efficiency of 7.2%. Numerical simulations showed excellent agreement with experimental results, confirming the high precision and designability of our method. Our lenses were also able to image objects with features down to 1.1 μm, showcasing their potential for practical applications in imaging. Our method is a cost-effective, simple, and scalable solution for mass production of planar lenses and other optical components operating in the visible region. It enables the development of advanced, miniaturized optical systems to meet modern technology demand, making it a valuable contribution to optical component manufacturing.

Abstract Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances. Current encoding schemes including time-bin, polarization, and orbital angular momentum, suffer from the lack of reconfigurability and thus scalability issues. Here, we demonstrate the first-time implementation of frequency-bin-encoded entanglement-based quantum key distribution and a reconfigurable distribution of entanglement using frequency-bin encoding. Specifically, we demonstrate a novel scalable frequency-bin basis analyzer module that allows for a passive random basis selection as a crucial step in quantum protocols, and importantly equips each user with a single detector rather than four detectors. This minimizes massively the resource overhead, reduces the dark count contribution, vulnerability to detector side-channel attacks, and the detector imbalance, hence providing an enhanced security. Our approach offers an adaptive frequency-multiplexing capability to increase the number of channels without hardware overhead, enabling increased secret key rate and reconfigurable multi-user operations. In perspective, our approach enables dynamic resource-minimized quantum key distribution among multiple users across diverse network topologies, and facilitates scalability to large-scale quantum networks.

Abstract The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs). The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states. Furthermore, a 3 × 3 PD-RRAM array is fabricated to demonstrate optical image recognition, achieving a universal architecture with ultralow latency and low power consumption. This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-efficient in-sensor computing primitive for future IoT applications.

Abstract Solution-processed quantum dot light-emitting diodes (QLEDs) hold great potential as competitive candidates for display and lighting applications. However, the serious energy disorder between the quantum dots (QDs) and hole transport layer (HTL) makes it challenging to achieve high-performance devices at lower voltage ranges. Here, we introduce “giant” fully alloy CdZnSe/ZnSeS core/shell QDs (size ~ 19 nm) as the emitting layer to build high-efficient and stable QLEDs. The synthesized CdZnSe-based QDs reveal a decreased ground-state band splitting, shallow valence band maximum, and improved quasi-Fermi level splitting, which effectively flatten the energy landscape between the QD layer and hole transport layer. The higher electron concentration and accelerated hole injection significantly promote the carrier radiative recombination dynamics. Consequently, CdZnSe-based device exhibits a high power conversion efficiency (PCE) of 27.3% and an ultra-low efficiency roll-off, with a high external quantum efficiency (EQE) exceeding 25% over a wide range of low driving voltages (1.8-3.0 V) and low heat generation. The record-high luminance levels of 1,400 and 8,600 cd m-2 are achieved at bandgap voltages of 100% and 120%, respectively. Meanwhile, These LEDs show an unprecedented operation lifetime T95 (time for the luminance to decrease to 95%) of 72,968 h at 1,000 cd m-2. Our work points to a novel path to flatten energy landscape at the QD-related interface for solution-processed photoelectronic devices.

Abstract Vortex beams are currently drawing a great deal of interest, from fundamental research to several promising applications. While their generation in bulky optical devices limits their use in integrated complex systems, metasurfaces have recently proven successful in creating optical vortices, especially in the linear regime. In the nonlinear domain, of strategic importance for the future of classical and quantum information, to date orbital angular momentum has only been created in qualitative ways, without discussing discrepancies between design and experimental results. Here, we demonstrate and analyze the generation of high-purity second harmonic (SH) optical vortices via dielectric meta-holograms. Through full-wave simulations and a proper fabrication protocol, we achieve efficient frequency doubling of an unstructured pump beam into SH vortices with topological charges from 1 to 10. Interferometric and modal-purity measurements confirm the generation of high-quality SH vortices with minimal deviations from the intended design thanks to a quasi-local control over the SH phase. Through systematic comparisons between experimental data and semi-analytical calculations, we also provide a clear insight into the occurrence of ghost vortices in the metasurface-generated harmonic beams, highlighting the importance of simple designs that can be readily transposed into fabricated devices with high fidelity. Our findings underscore the potential of nonlinear dielectric metasurfaces for versatile structured-light generation and manipulation, paving the way for future developments in integrated photonic systems.

Abstract Hanbury-Brown and Twiss (HBT) effect is the foundation for stellar intensity interferometry. However, it is a phase insensitive two-photon interference effect. Here we extend the HBT interferometer by mixing intensity-matched reference fields with the input fields before intensity correlation measurement. With the freely available coherent state serving as the reference field, we experimentally demonstrate the phase sensitive two-photon interference effect when the input fields are thermal fields in either continuous wave or non-stationary pulsed wave and measure the complete complex second-order coherence function of the input fields without bringing them together from separate locations. Moreover, we discuss how to improve the signal level by using the more realistic continuous wave broadband anti-bunched light fields as the reference field. Our investigations pave the way for developing new technology of remote sensing and interferometric imaging with applications in long baseline high-resolution astronomy.

This paper presents a convolutional neural network (CNN) model, enhanced with the convolutional block attention module (CBAM), designed to accurately predict the beam quality factor M2, and numerical aperture (NA) of photonic crystal fibers. The integration of CBAM significantly improves the model's feature extraction capability by enabling it to focus on key features and filter out irrelevant information. Simulation results demonstrate that the model achieves a mean relative error of only 0.381% for M2 and 2.293% for NA, outperforming convolutional models without attention mechanisms. With a prediction time of approximately 7 ms, the model allows for rapid and efficient predictions of M2 and NA. Moreover, when the noise factor remains below 0.32, the model's prediction error shows minimal fluctuation, highlighting its robustness. Comparative experimental analysis further validates the model's effectiveness. This approach offers a reliable and efficient solution for fast, accurate measurement of M² and NA, with significant implications for the prediction and analysis of beam performance in various applications.

Excellent disturbance rejection ability is essential for a photoelectric tracking system (PTS) based on the Charge-Couple Device (CCD) sensor, which is a premise guarantee for obtaining highly accurate tracking, especially under the condition of moving carriers with intense disturbances. The feedforward control method based on disturbance estimated from the system model output and sensor output is currently a commonly used strategy for disturbance compensation due to its ability to directly counteract the disturbances. This disturbance feedforward compensation method behaves sensitively in case of model mismatch caused by internal disturbances, which may lead to a significant reduction in the disturbance compensation effect or even cause system instability. In this paper, unlike disturbance feedforward compensation, a pure disturbance sliding-mode feedback control (DSMFBC) based on disturbance observation without additional sensors is proposed, ensuring faster and precise compensation for disturbance. In case of model mismatch, the observed disturbance is used to build the disturbance feedback control to maintain a more efficient disturbance compensation through the robustness that feedback naturally possesses. To achieve a stronger anti-disturbance capability, a sliding-mode nonlinear control method is used to design the control law. The experimental setup of PTS based on the fast-steering mirror (FSM) demonstrates that the method has better dynamic performance and disturbance rejection ratio.