Xiangzhao Wang

Double-Ronchi shearing interferometry is a promising wavefront aberration measurement system for advanced lithography projection lens systems. The image grating defocusing is a key systematic error of the interferometer. However, the effects and elimination of this error have not been systematically researched. In this work, the interference field effects caused by the image grating defocusing are analyzed based on the theories of scalar diffraction, and a method to eliminate the effects is proposed. The theoretical analysis has been verified by a simulation and experiments. The results show that the error of image grating defocusing is mainly expressed as the Zernike Z₄ term and Z₉ term in the reconstructed wavefront, and the coefficients of Z₄ , and Z₉ , respectively, are related to NA² , NA⁴ , and the defocus distance z . When the numerical aperture (NA) of the under-test projection lens is 0.6, 99.8384% of the errors caused by the image grating defocusing can be removed. When the NA is reduced to 0.3, 99.9854% of the errors can be removed. Additionally, when the NA is less than 0.1, almost all the errors can be eliminated.

Optical coherence tomography (OCT) provides unique advantages in ophthalmic examinations owing to its noncontact, high-resolution, and noninvasive features, which have evolved into one of the most crucial modalities for identifying and evaluating retinal abnormalities. Segmentation of laminar structures and lesion tissues in retinal OCT images can provide quantitative information on retinal morphology and reliable guidance for clinical diagnosis and treatment. Convolutional neural networks (CNNs) have achieved success in various medical image segmentation tasks. However, the receptive field of convolution has inherent locality constraints, resulting in limitations of mainstream frameworks based on CNNs, which is still evident in recognizing the morphological changes of retina OCT. In this study, we proposed an end-to-end network, TranSegNet, which incorporates a hybrid encoder that combines the advantages of a lightweight vision transformer (ViT) and the U-shaped network. The CNN features under multiscale resolution are extracted based on the improved U-net backbone, and a ViT with the multi-head convolutional attention is introduced to capture the feature information in a global view, realizing accurate localization and segmentation of retinal layers and lesion tissues. The experimental results illustrate that hybrid CNN-ViT is a strong encoder for retinal OCT image segmentation tasks and the lightweight design reduces its parameter size and computational complexity while maintaining its outstanding performance. By applying TranSegNet to healthy and diseased retinal OCT datasets separately, TranSegNet demonstrated superior efficiency, accuracy, and robustness in the segmentation results of retinal layers and accumulated fluid than the four advanced segmentation methods, such as FCN, SegNet, Unet and TransUnet.

Double-Ronchi shearing interferometry is a promising technique for in situ wavefront aberration measurement of the projection lens in photolithography systems. In practice, the non-uniformity of illumination is an important issue affecting the interference field, which has not been systematically researched. In this work, the interference field errors caused by non-uniform illumination distributions are analyzed utilizing the theories of scalar diffraction. The theoretical analysis has been verified by simulation and fundamental experiments. Results show that the uniformity requirements for the abrupt annular, Gaussian, and uniform random illumination distribution (RD) are 0.9434, 0.8439, and 0.2751, respectively, with a shear ratio of 5% and a relative wavefront reconstruction error of 1%. The uniformity of the three distributions is reduced to 0.6513, 0.5864, and 0.1234, respectively, with the shear ratio shrunk to 3%. When the shear ratio is less than 1%, there is no specific requirement for illumination uniformity.

In the optical coherence tomography system, the mismatch in dispersion between the two interference arms will reduce the axial resolution of the system. Therefore, dispersion compensation is required. However, the current depth-resolved dispersion compensation method uses a fixed window function to extract signals at different depths, which has phase calculation error and reduces the accuracy of dispersion compensation. In this paper, we optimized the rectangular window function for the signal interception. According to the functional relationship between the phase and the wave number in the presence of dispersion, the second-order fit to the obtained phase is performed and the standard error of the fit result is used as the criterion. The optimized rectangular window is used to intercept the signal and perform FFT, thereby effectively reducing the phase error caused by the window function. And we accurately extracted the dispersion coefficients of samples to improve the accuracy of dispersion compensation.

Extreme ultraviolet (EUV) multilayer defects result in the degradation of through-focus imaging quality. The optical proximity effect is another crucial factor that degrades the imaging quality. Both the impacts of the defects and the optical proximity effects could be mitigated by modifying the original mask patterns. A heuristic-based defect compensation method considering optical proximity correction and through-focus optimization is proposed in this paper. The edge of the mask pattern and the insertion of sub-resolution assist features (SRAFs) are optimized by covariance matrix adaptation evolution strategy (CMA-ES) to compensate for the degradation of the imaging quality with a certain defocus range. New encoding strategies for the edge pixels of the mask pattern and the SRAFs are proposed and utilized in this paper to ensure the manufacturability of the mask and the efficiency of the optimization at the same time. The rigorous database approach based on the scattering matrix is adopted to simulate the mask diffraction spectrum efficiently. Simulations verify that the through-focus imaging quality of both the defective masks with bump defects and pit defects could be obviously improved by the proposed defect compensation method.

As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic printability of the design has become one of the important issues in IC design and manufacturing. There are patterns that cause lithography hotspots in the IC layout. Hotspot detection affects the turn-around time and the yield of IC manufacturing. The precision and F1 score of available machine-learning-based hotspot-detection methods are still insufficient. In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep convolutional neural network is proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. The proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. The results show that updating the weights of partial convolutional layers has little effect on the results of this method.

Ronchi shearing interferometry is a promising technique for in situ wavefront aberration measurement of the projection lens in advanced photolithography systems. The Van Cittert–Zernike theorem has been used to analyze the interference signal of a Ronchi shearing interferometer in the effective interference area (overlapping area of the ± 1 st diffraction orders produced by the image grating). However, the applicability of this theorem has not been systematically studied. In this work, the analytical expression of the Ronchigram in this area is derived based on the theory of scalar diffraction and incoherent imaging. The results show that only the object and image grating with infinite diffraction orders can fully satisfy the Van Cittert–Zernike theorem. In the finite diffraction orders case, the theorem can be considered approximately applicable in the overlapping area of the ± 3 rd diffraction orders produced by the image grating. The applicable area extends to the overlapping area of the ± 2 nd diffraction orders under a shear ratio of less than 1%, which accounts for 97% of the effective interference area. The theoretical analysis has been verified by simulation and fundamental experiments.

Optical coherence tomography (OCT) is a high-resolution and non-invasive optical imaging technology, which is widely used in many fields. Nevertheless, OCT images are disturbed by speckle noise due to the low-coherent interference properties of light, resulting in significant degradation of OCT image quality. Therefore, a denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary is proposed in this paper. To remove noise and improve image quality, the algorithm first constructs a global dictionary from high-quality OCT images as training samples and then estimates the noise intensity for each input image. Finally, the OCT images are sparsely decomposed and reconstructed according to the global dictionary and noise intensity. Experimental results indicate that the proposed algorithm efficiently removes speckle noise from OCT images and yield high-quality images. The denoising effect and execution efficiency are evaluated based on quantitative metrics and running time, respectively. Compared with the mainstream adaptive dictionary denoising algorithm in sparse representation and other denoising algorithms, the proposed algorithm exhibits satisfying results in terms of speckle-noise reduction as well as edge preservation, at a reduced computational cost. Moreover, the final denoising effect is significantly better for sets of images with significant variations in noise intensity.

Lateral shearing based on the grating is one of the classical configurations when measuring the wavefront aberration of optical systems such as the lithographic projection lens. Because the wavefront under test is spherical, but a detector surface is a plane, the coordinate of the wavefront surface will be distorted on the detector surface. As the numerical aperture (NA) of the optics under test increases, the shear ratios at different positions within the shearing region are significantly different due to the coordinate distortion. Therefore, the reconstructed wavefront from the traditional lateral-shearing reconstruction method designed for a fixed shearing ratio will contain a non-negligible error. In this work, we use the ray-tracing method to calculate the shearing ratio distribution in the shearing region and propose a compensated differential Zernike fitting method to solve the coordinate distortion and shearing ratio variation problem. The relative error of the uncompensated result will increase as the NA increases. This error is around 1% for a 0.1 NA, 10% for a 0.3 NA, and over 100% for an NA above 0.7. Compensation for the shearing ratio variation is necessary when the NA is larger than 0.3. The proposed method has been validated by simulations and experiments.

A fast mask model for extreme ultraviolet (EUV) lithography is vital to process simulation and resolution enhancement techniques. As the target pattern sizes have decreased, the impact of the absorber sidewall angle (SWA) has become a serious problem. In order to model the EUV mask with a slanted absorber sidewall quickly and accurately, a fast mask model based on the absorber sublayer decomposition is proposed. Since the absorber sidewall is slanted but not perpendicular to the multilayer surface, the absorber is decomposed into several thin pattern layers. For each thin layer, the diffraction is calculated by the edge point pulses model. The light propagation between two layers is calculated by spectrum superposition in the frequency domain with Hopkins frequency shift. The fast EUV mask model with slanted absorber sidewall is established by combining the accurate absorber model and the equivalent layer multilayer model. Simulations and comparisons validate the effectiveness of the proposed model. For the 22 nm vertical line-space pattern, the calculation errors of critical dimension via the proposed model are lower than 0.05 nm for different SWA values.