Phase demodulation method of high line density grating interferometric signal based on wavelet transform.

The increasing line density of the reference grating and the accelerating miniaturization of ultra-precision displacement measurement technology necessitate more stable interferometric signal processing methods for high line density gratings, particularly in low signal-to-noise ratio scenarios. This paper presents a phase demodulation method for dynamic interferometric signals for high line density gratings. The Morlet wavelet transform is utilized to obtain the instantaneous frequency of the interferometric signal, integration of which yields the relative displacement, while adding adjacent relative displacements without gaps provides the absolute displacement during dynamic motion of the grating. In simulations with a signal-to-noise ratio ranging from 40 to 70 dB, the proposed method demonstrates greater robustness compared to the traditional method. By establishing a platform for repeated experiments and comparing it with traditional methods, it was found that the maximum deviation between calculation results obtained using this method and traditional methods is 0.8 nm, further confirming its potential application.

Comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers.

In recent years, attention has been directed towards cost-effective and compact freeform Schwarzschild imaging spectrometers with plane gratings. The utilization of tolerance analysis serves as a potent approach to facilitate the development of prototypes. Conventional tolerance analysis methods often rely solely on the modulation transfer function (MTF) criterion. However, for a spectrometer system, factors such as the keystone/smile distortion and spectral resolution performance also require consideration. In this study, a tailored comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers was developed, considering vital aspects such as the MTF, keystone/smile distortion, and spectral resolution. Through this approach, meticulous tolerance analysis was conducted for a freeform Schwarzschild imaging spectrometer, providing valuable insights for the prototype machining and assembly processes. Emphasis was placed on the necessity of precise control over the tilt and decenter between the first and third mirrors, whereas the other fabrication and assembly tolerances adhered to the standard requirements. Finally, an alignment computer-generated hologram (CGH) was employed for the preassembly of the first and third mirrors, enabling successful prototype development. The congruence observed between the measured results and tolerance analysis outcomes demonstrates the effectiveness of the proposed method.

Real-time synchronized monitoring for the overlap accuracy of the combining beam spot in a wavelength beam combination based on confocal planes.

Beam overlap accuracy in a wavelength beam combination system determines the beam quality and efficiency, so systematic monitoring of overlap accuracy is essential. In this work, a method of performing real-time synchronized monitoring and recording overlap accuracy for a combining beam spot is proposed. Firstly, theoretical calculations for monitoring different wavelength sub-beam positions and angular errors are established. Then, an optical design and grayscale centroid algorithm are developed to analyze and simulate the combination spots. A monitoring device was designed and constructed to meet the requirements of combining system applications, which achieved an accuracy of 8.86 µrad. Finally, the method successfully monitored the system spot fluctuation range within ±22 µrad. This study resolves the issue of distinguishing the different wavelength sub-beams and their response delays in traditional combining beams. It offers precise error data for real-time synchronized calibration of the overlap accuracy in laser beam combining technology.

Improving grating duty cycle uniformity: amplitude-splitting flat-top beam laser interference lithography.

Laser interference lithography is an effective approach for grating fabrication. As a key parameter of the grating profile, the duty cycle determines the diffraction characteristics and is associated with the irradiance of the exposure beam. In this study, we developed a fabrication technique amplitude-splitting flat-top beam interference lithography to improve duty cycle uniformity. The relationship between the duty cycle uniformity and irradiance of the exposure beam is analyzed, and the results indicate that when the beam irradiance nonuniformity is less than 20%, the grating duty cycle nonuniformity is maintained below ±2%. Moreover, an experimental amplitude-splitting flat-top beam interference lithography system is developed to realize an incident beam irradiance nonuniformity of 21%. The full-aperture duty cycle nonuniformity of the fabricated grating is less than ±3%. Amplitude-splitting flat-top beam interference lithography improves duty cycle uniformity, greatly reduces energy loss compared to conventional apodization, and is more suitable for manufacturing highly uniform gratings over large areas.

Grayscale-patterned integrated multilayer-metal-dielectric microcavities for on-chip multi/hyperspectral imaging in the extended visible bandwidth.

Pixelated filter arrays of Fabry-Perot (FP) cavities are widely integrated with photodetectors to achieve a WYSIWYG ("what you see is what you get") on-chip spectral measurements. However, FP-filter-based spectral sensors typically have a trade-off between their spectral resolution and working bandwidth due to design limitations of conventional metal or dielectric multilayer microcavities. Here, we propose a new idea of integrated color filter arrays (CFAs) consisting of multilayer metal-dielectric-mirror FP microcavities that, enable a hyperspectral resolution over an extended visible bandwidth (∼300 nm). By introducing another two dielectric layers on the metallic film, the broadband reflectance of the FP-cavity mirror was greatly enhanced, accompanied by as-flat-as-possible reflection-phase dispersion. This resulted in balanced spectral resolution (∼10 nm) and spectral bandwidth from 450 nm to 750 nm. In the experiment, we used a one-step rapid manufacturing process by using grayscale e-beam lithography. A 16-channel (4 × 4) CFA was fabricated and demonstrated on-chip spectral imaging with a CMOS sensor and an impressive identification capability. Our results provide an attractive method for developing high-performance spectral sensors and have potential commercial applications by extending the utility of low-cost manufacturing process.

Superposition Fabry-Perot filter array for a computational hyperspectral camera.

Computational hyperspectral cameras with broadband encoded filter arrays enable high precision spectrum reconstruction with only a few filters. However, these types of hyperspectral cameras have limited application, because it is difficult for conventional encoded filter arrays to balance among the spectrum regulation capacity, angle insensitivity, and processibility. This Letter presents a new, to the best of our knowledge, encoded filter composed of superposition Fabry-Perot resonance cavity (SFP) that can simultaneously take all three aspects into consideration. By learning the parameters of an SFP encoder and a neural network decoder in an end-to-end manner, a computational hyperspectral camera based on an SFP filter array presents up to 2.24 times higher spectral reconstruction accuracy, 10 times wider working angle, and can be produced with a low-cost manufacturing process.

All-day thin-lens computational imaging with scene-specific learning recovery.

Modern imaging optics ensures high-quality photography at the cost of a complex optical form factor that deviates from the portability. The drastic development of image processing algorithms, especially advanced neural networks, shows great promise to use thin optics but still faces the challenges of residual artifacts and chromatic aberration. In this work, we investigate photorealistic thin-lens imaging that paves the way to actual applications by exploring several fine-tunes. Notably, to meet all-day photography demands, we develop a scene-specific generative-adversarial-network-based learning strategy and develop an integral automatic acquisition and processing pipeline. Color fringe artifacts are reduced by implementing a chromatic aberration pre-correction trick. Our method outperforms existing thin-lens imaging work with better visual perception and excels in both normal-light and low-light scenarios.

Modeling classical wavefront sensors.

We present an image formation model for deterministic phase retrieval in propagation-based wavefront sensing, unifying analysis for classical wavefront sensors such as Shack-Hartmann (slopes tracking) and curvature sensors (based on Transport-of-Intensity Equation). We show how this model generalizes commonly seen formulas, including Transport-of-Intensity Equation, from small distances and beyond. Using this model, we analyze theoretically achievable lateral wavefront resolution in propagation-based deterministic wavefront sensing. Finally, via a prototype masked wavefront sensor, we show simultaneous bright field and phase imaging numerically recovered in real-time from a single-shot measurement.

Ultra-high resolution coded wavefront sensor.

Wavefront sensors and more general phase retrieval methods have recently attracted a lot of attention in a host of application domains, ranging from astronomy to scientific imaging and microscopy. In this paper, we introduce a new class of sensor, the Coded Wavefront Sensor, which provides high spatio-temporal resolution using a simple masked sensor under white light illumination. Specifically, we demonstrate megapixel spatial resolution and phase accuracy better than 0.1 wavelengths at reconstruction rates of 50 Hz or more, thus opening up many new applications from high-resolution adaptive optics to real-time phase retrieval in microscopy.

Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence.

Species discrimination of insects is an important aspect of ecology and biodiversity research. The traditional methods based on human visual experience and biochemical analysis cannot strike a balance between accuracy and timeliness. Morphological identification using computer vision and machine learning is expected to solve this problem, but image features have poor accuracy for very similar species and usually require complicated networks that are unfriendly to portable edge devices. In this work, we propose a fast and accurate species discrimination method of similar insects using hyperspectral features and lightweight machine learning algorithm. Feature regions selection, feature spectra selection and model quantification are used for the optimization of discriminating network. The experimental results of six similar butterfly species in the genus of Graphium show that, compared with morphological recognition with machine vision, our work achieves a higher accuracy of 92.36 ± 3.04% and a shorter inference time of 0.6 ms, with the tiny-size convolutional neural network deployed on a neural network chip. This study provides a rapid and high-accuracy species discrimination method for insects with high appearance similarity and paves the way for field discriminations using intelligent micro-spectrometer based on on-chip microstructure and artificial intelligence chip.

Reconfigurable Metalens with Phase-Change Switching between Beam Acceleration and Rotation for 3D Depth Imaging.

Depth imaging is very important for many emerging technologies, such as artificial intelligence, driverless vehicles and facial recognition. However, all these applications demand compact and low-power systems that are beyond the capabilities of most state-of-art depth cameras. Recently, metasurface-based depth imaging that exploits point spread function (PSF) engineering has been demonstrated to be miniaturized and single shot without requiring active illumination or multiple viewpoint exposures. A pair of spatially adjacent metalenses with an extended depth-of-field (EDOF) PSF and a depth-sensitive double-helix PSF (DH-PSF) were used, using the former metalens to reconstruct clear images of each depth and the latter to accurately estimate depth. However, due to these two metalenses being non-coaxial, parallax in capturing scenes is inevitable, which would limit the depth precision and field of view. In this work, a bifunctional reconfigurable metalens for 3D depth imaging was proposed by dynamically switching between EDOF-PSF and DH-PSF. Specifically, a polarization-independent metalens working at 1550 nm with a compact 1 mm2 aperture was realized, which can generate a focused accelerating beam and a focused rotating beam at the phase transition of crystalline and amorphous Ge2Sb2Te5 (GST), respectively. Combined with the deconvolution algorithm, we demonstrated the good capabilities of scene reconstruction and depth imaging using a theoretical simulation and achieved a depth measurement error of only 3.42%.

Quantitative Phase and Intensity Microscopy Using Snapshot White Light Wavefront Sensing.

Phase imaging techniques are an invaluable tool in microscopy for quickly examining thin transparent specimens. Existing methods are limited to either simple and inexpensive methods that produce only qualitative phase information (e.g. phase contrast microscopy, DIC), or significantly more elaborate and expensive quantitative methods. Here we demonstrate a low-cost, easy to implement microscopy setup for quantitative imaging of phase and bright field amplitude using collimated white light illumination.

Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification.

Convolutional neural networks (CNNs) excel in a wide variety of computer vision applications, but their high performance also comes at a high computational cost. Despite efforts to increase efficiency both algorithmically and with specialized hardware, it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Here we explore a complementary strategy that incorporates a layer of optical computing prior to electronic computing, improving performance on image classification tasks while adding minimal electronic computational cost or processing time. We propose a design for an optical convolutional layer based on an optimized diffractive optical element and test our design in two simulations: a learned optical correlator and an optoelectronic two-layer CNN. We demonstrate in simulation and with an optical prototype that the classification accuracies of our optical systems rival those of the analogous electronic implementations, while providing substantial savings on computational cost.