High-precision wavefront measurement is a crucial technology in lithography systems. Ronchi lateral-shearing interferometry (LSI) has exhibited significant potential in wavefront measurement of the projection lenses in lithography systems due to its advantages of a common optical path, null testing, and reference-free interference. A conventional Ronchi interferometer applies two orthogonal Ronchi gratings sequentially as beam-splitting elements to obtain shear information in two directions. However, system errors from grating switching and higher-order parasitic diffraction complicate the accurate calculation of the wavefront under test. This paper proposes an LSI based on the sinusoidal amplitude grating. By altering the system's coherent modulation function, only the 0th and ±1st orders interfere, avoiding the effects of irrelevant diffraction orders. We establish an interference model of this novel LSI using scalar diffraction theory and used a combination of uniform phase shift and least squares to reconstruct the wavefront with high accuracy. This work simplifies the operating principle of Ronchi LSI, theoretically eliminating error sources such as higher-order parasitic diffraction and even-order harmonic diffraction, providing more possibilities for structural improvements in Ronchi LSI.
The hybrid refractive-diffractive optics system exhibits strong capabilities in achromatic and non-thermalized design, as well as information encoding. This paper introduces an innovative end-to-end design scheme for refractive-diffractive hybrid imaging optical systems, which optimizes both optical and neural network parameters simultaneously. An all-ray differentiable ray-tracing model is proposed to integrate lens and diffractive optical elements into a unified design framework, thereby maintaining precision by avoiding wave-to-ray conversion losses. The neural network is constructed based on the imaging characteristics of infrared hybrid optical systems and enhances aberration correction. The proposed method is applied to the design of a single-lens short-wave infrared imaging system, outperforming traditional discrete designs and demonstrating significant potential for infrared optical system applications.
Wavefront aberration is a crucial metric for evaluating the imaging quality of an optical system and enhancing the accuracy of wavefront detection is of significant importance. Noise is a critical factor that affects detection accuracy. Simulating and suppressing noise can help explore the theoretical limit of wavefront detection and improve the actual measurement accuracy. We develop a comprehensive noise model where the input is a simulated, noise-free image in units of photons, and the output is a noisy digital signal. The model considers external disturbance noise, speckle noise, and camera noise. Speckle noise is selectively added based on the light source’s coherence. Camera noise is modeled using real camera parameters and includes photon shot noise, dark shot noise, readout noise, and quantization noise. Additionally, a noise suppression algorithm based on frame averaging is designed. We introduce the concept of a noise suppression factor, calculate this factor based on the noise characteristics and system properties, and apply it to the frame-averaged noisy image on a pixel-by-pixel basis, achieving effective noise reduction. Using the established noise model, we calculate the theoretical peak-to-valley (PV) and root mean square (RMS) limit determined by noise for two typical high-precision wavefront aberration detection systems: the Ronchi lateral shearing interferometry (LSI) system and the phase-diverse phase retrieval (PDPR) system. With our proposed noise suppression algorithm, the theoretical RMS limit can be reduced to 10% of the previous value, demonstrating its effectiveness in noise suppression. Our model provides a definitive standard for the theoretical accuracy limit of optical metrology, guiding the selection of hardware and the design of wavefront detection algorithms for subsequent research.
Aberrations in minimalist optical imaging systems pose significant challenges to achieving high-quality imaging. Traditional Wiener filtering methods, though effective, are constrained by their dependency on precise blur kernels and noise models, and their performance degrades with spatial variations in these parameters. On the other hand, deep learning techniques often fail to fully utilize prior information about aberrations and suffer from limited interpretability. To address these limitations, we propose a novel deep attention Wiener network (DAWN). This approach integrates deep learning with Wiener filtering to enhance image restoration while reducing computational complexity. By using optical simulations to generate blur kernels and noise models that closely mirror real conditions, our method fits distinct point spread function (PSF) for different fields of view (FOV), creating a robust dataset for training. The DAWN model first employs a convolutional neural network (CNN) for feature extraction, followed by sequential Wiener filtering applied in half FOV block length steps. To further improve image restoration, a nonlinear activation free net (NAFNet) is used to correct discrepancies introduced by simulated blur kernels and noise models. The model is trained end-to-end, and to streamline the process, Wiener filtering is confined to 4 × 4 FOV blocks. A weighting matrix within the Wiener filtering layer mitigates seams between adjacent blocks. Simulation and experiment results demonstrate that our approach outperforms the mainstream image restoration methods.
Traditional phase retrieval methods for Ronchi lateral shearing interferometry eliminate the impact of high diffraction orders by increasing the number of phase-shifting interferograms, however, this introduces additional error in the phase-shifting process. We propose an optimization method combining a 2-frame phase-shifting algorithm to achieve accurate wavefront reconstruction. A numerical model matching the physical model is constructed and the cross-iterative gradient descent method is used to optimize the initial results obtained by the two-step phase-shifting method. The accuracy and robustness of the method are verified by simulations and experiments. The proposed method has the advantages of achieving high-precision wavefront reconstruction and correcting the phase-shifting errors, and it significantly simplifies the process of phase shift.
The kinematic and morphological abnormalities can be used for accurate detection of myocardial infarction without contrast agents. It has important implications for the early treatment of patients. However, methods based on motion tracking are time-consuming, and the complex movements of the heart make them difficult to implement. In this paper, we propose a deep learning constrained framework based on relative motion features. It can detect myocardial infarction areas through cine cardiac magnetic resonance imaging(CMRI) images. It includes one relative motion extraction component and one deep neural network component. In the relative motion model, a U-Net model is used to segment the myocardial contour. After that, the motion features and pixel features of the myocardium are extracted and fused. Finally, the extracted relative features are further learned via the deep neural network model based on ConvLSTM to predict the myocardial infarction area. Our method doesn’t need a pre-find position match and is more suitable for the physiological process of the myocardium. We validated the performance of our framework in 276 cine CMRI sequences datasets, and it yielded a high consistency with manual delineation (90.8% detection accuracy). The results demonstrate that our proposed method can be an attractive tool for the diagnosis of myocardial infarction in the clinic.
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