Distortion measurement of a lithography projection lens based on multichannel grating lateral shearing interferometry.

The optical distortion of the lithographic projection lens can reduce imaging quality and cause overlay errors in lithography, thus preventing the miniaturization of printed patterns. In this paper, we propose a technique to measure the optical distortion of a lithographic projection lens by sensing the wavefront aberrations of the lens. A multichannel dual-grating lateral shearing interferometer is used to measure the wavefront aberrations at several field points in the pupil plane simultaneously. Then, the distortion at these field points is derived according to the proportional relationship between the Z 2 and Z 3 Zernike terms (the tilt terms) and the image position shifts. Without the need for additional devices, our approach can simultaneously retrieve both the wavefront aberrations and the image distortion information. Consequently, it improves not only measurement speed and accuracy but also enables accounting for displacement stage positioning error. Experiments were conducted on a lithographic projection lens with a numerical aperture of 0.57 to verify the feasibility of the proposed method.

Effects and elimination of image grating defocusing on a double-Ronchi shearing interference field.

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.

TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography.

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.

Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography.

The multilayer defects of mask blanks in extreme ultraviolet (EUV) lithography may cause severe reflectivity deformation and phase shift. The profile information of a multilayer defect is the key factor for mask defect compensation or repair. This paper introduces an artificial neural network framework to reconstruct the profile parameters of multilayer defects in the EUV mask blanks. With the aerial images of the defective mask blanks obtained at different illumination angles and a series of generative adversarial networks, the method enables a way of multilayer defect characterization with high accuracy.

Super-resolution reconstruction algorithm for optical-resolution photoacoustic microscopy images based on sparsity and deconvolution.

The lateral resolution of the optical-resolution photoacoustic microscopy (OR-PAM) system depends on the focusing diameter of the probe beam. By increasing the numerical aperture (NA) of optical focusing, the lateral resolution of OR-PAM can be improved. However, the increase in NA results in smaller working distances, and the entire imaging system becomes very sensitive to small optical imperfections. The existing deconvolution-based algorithms are limited by the image signal-to-noise ratio when improving the resolution of OR-PAM images. In this paper, a super-resolution reconstruction algorithm for OR-PAM images based on sparsity and deconvolution is proposed. The OR-PAM image is sparsely reconstructed according to the constructed loss function, which utilizes the sparsity of the image to combat the decrease in the resolution. The gradient accelerated Landweber iterative algorithm is used to deconvolve to obtain high-resolution OR-PAM images. Experimental results show that the proposed algorithm can improve the resolution of mouse retinal images by approximately 1.7 times without increasing the NA of the imaging system. In addition, compared to the Richardson-Lucy algorithm, the proposed algorithm can further improve the image resolution and maintain better imaging quality, which provides a foundation for the development of OR-PAM in clinical research.

Through-focus EUV multilayer defect compensation considering optical proximity correction.

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.

Optical coherence elastography to evaluate depth-resolved elasticity of tissue.

Skin-elasticity measurements can assist in the clinical diagnosis of skin diseases, which has important clinical significance. Accurately determining the depth-resolved elasticity of superficial biological tissue is an important research direction. This paper presents an optical coherence elastography technique that combines surface acoustic waves and shear waves to obtain the elasticity of multilayer tissue. First, the phase velocity of the high-frequency surface acoustic wave is calculated at the surface of the sample to obtain the Young's modulus of the top layer. Then, the shear wave velocities in the other layers are calculated to obtain their respective Young's moduli. In the bilayer phantom experiment, the maximum error in the elastic estimation of each layer was 2.2%. The results show that the proposed method can accurately evaluate the depth-resolved elasticity of layered tissue-mimicking phantoms, which can potentially expand the clinical applications of elastic wave elastography.

Compensated differential Zernike fitting method for wavefront aberration metrology based on grating lateral shearing.

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.

Applicability of the Van Cittert-Zernike theorem in a Ronchi shearing interferometer.

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 ±1st 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 ±3rd diffraction orders produced by the image grating. The applicable area extends to the overlapping area of the ±2nd 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.

Denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary.

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.

Effects of illumination non-uniformity on the double-Ronchi lateral shearing interference field.

Double-Ronchi shearing interferometry is a promising technique for insitu 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.

Sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation for wafer alignment.

Wafer alignment is the core technique of lithographic tools. Image-processing-based wafer alignment techniques are commonly used in lithographic tools. An alignment algorithm is used to analyze the alignment mark image for obtaining the mark position. The accuracy and speed of the alignment algorithm are very important for guaranteeing the overlay and throughput of lithographic tools. The most commonly used algorithm in image-processing-based alignment techniques is the self-correlation method. This method has a high accuracy, but the calculation is complex, and the calculation speed is slow. In this paper, we propose a sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation. The algorithm first reconstructs the alignment signal by sampling theorem interpolation and then obtains the sub-pixel position of the mark by Gaussian fitting. The accuracy and robustness of the algorithm are verified by testing the simulated marks and experimentally captured marks. The repeat accuracy can reach 1/100 pixels, which is in the same level with the self-correlation method. The calculation speed is highly improved compared with the self-correlation method, which needs only about 1/3 of even short calculation time.

High NA objective lens wavefront aberration measurement using a cat-eye retroreflector and Zernike polynomial.

Wavefront aberration is one important parameter for objective lenses. When the NA (Numerical Aperture) of the objective lens becomes larger than 0.8, wavefront aberration measurement with high accuracy and low cost is difficult to realize because of the lack of a reference sphere. In this paper, a new method is proposed to measure the wavefront aberration of a high NA objective lens. A cat-eye retroreflector with a plane mirror is used to reflect the wavefront. The plane mirror is tilted in at least three different directions by certain tilt angles to collect sufficient information of the wavefront aberration under test. Specific grid-combined Zernike polynomial is built for each set of tilt angles and directions to fit the corresponding returned wavefronts. The wavefront aberration can be reconstructed from the fitting results of the returned wavefronts. The measurement accuracy is influenced by the tilt angle, tilt angle error, NA, defocus amount of the plane mirror, detector's resolution, and other random noise. The tilt angle error is the main source of the measurement error. The relative measurement error is within 5% and 1% when the relative tilt angle error is below 0.5% and 0.1% respectively. The feasibility of the proposed method is verified experimentally by measuring the wavefront aberrations of 0.14 NA, 0.65 NA, and 0.9 NA objective lenses. Wavefront aberration measurement for a high NA objective lens with high accuracy and low cost is achievable through this method.

Fast mask model for extreme ultraviolet lithography with a slanted absorber sidewall.

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.

Measurement algorithm for wafer alignment based on principal component analysis.

We propose a novel measurement algorithm for wafer alignment technology based on principal component analysis (PCA) of a mark image. The waveform of the mark is extracted from the enlarged mark image, which is collected by CCD. The position of the mark center on the CCD can be calculated based on the extracted waveform. By applying PCA to the mark image, the first principal component containing position information of the mark can be obtained. Therefore PCA can be used to extract the waveform from the mark image. Compared with the typical waveform extraction method (the summed projection (SP) method), the proposed PCA method can use the position information contained in the mark image more effectively. Through simulation and experiment, it is proved that the proposed PCA method can improve the contrast of the normalized waveform, and then improve the alignment accuracy.

Fast heuristic-based source mask optimization for EUV lithography using dual edge evolution and partial sampling.

Extreme ultraviolet (EUV) lithography is essential in the advanced technology nodes. Source mask optimization (SMO) for EUV lithography, especially the heuristic-based SMO, is one of the vital resolution enhancement techniques (RET). In this paper, a fast SMO method for EUV based on dual edge evolution and partial sampling strategies is proposed to improve the optimization efficiency and speed of the heuristic algorithm. In the source optimization (SO) stage, the position and intensity of the source points are optimized in turn. Using the sparsity of the optimized source, a partial sampling encoding method is applied to decrease the variables' dimension in optimization. In the mask optimization (MO) stage, the main features (MF) and the sub-resolution assistant features (SRAF) are optimized in turn. A dual edge evolution strategy is used in the MF optimization and the partial sampling encoding method is used in SRAF optimization. Besides, the imaging qualities at different focal planes are improved by SRAF optimization. The optimization efficiency is greatly improved by the dimensionality reduction strategies. Simulations are carried out with various target patterns. Results show the superiority of the proposed method over the previous method, especially for large complex patterns.

Extreme ultraviolet phase defect characterization based on complex amplitudes of the aerial images.

The profile deformation of a phase defect in an extreme ultraviolet (EUV) mask blank is the key factor to simulate its optical effects accurately and to compensate for it precisely. This paper provides a new, to the best of our knowledge, profile characterization method based on complex amplitudes of the aerial images for phase defects in EUV mask blanks. Fourier ptychography is adopted to retrieve the complex amplitudes of the aerial images and improve the lateral resolution. Both amplitude and phase impacted by the defect are taken into consideration to reconstruct the defect profile parameters (the height and the full width at half maxima of the defect's top and bottom profiles). A conformal convolutional neural network model is constructed to map the amplitudes and phases of aerial images to the defect profile parameters. The Gaussian-shaped defect models with the mapped profile parameters can be used to simulate the amplitude and phase properties of the defects when compensating for them. The proposed method is verified to reconstruct the defect profile parameters of both bump defects and pit defects accurately. The involvement of both the amplitude and phase information makes the reconstructed defect profile parameters more appropriate to simulate the optical effects of the defects.

Efficient optical proximity correction based on virtual edge and mask pixelation with two-phase sampling.

Optical proximity correction (OPC) is a widely used resolution enhancement technique (RET) in optical lithography to improve the image fidelity and process robustness. The efficiency of OPC is very important, especially for full-chip modification with complicated circuit layout in advanced technology nodes. An efficient OPC method based on virtual edge and mask pixelation with two-phase sampling is proposed in this paper. All kinds of imaging distortions are classified into two categories of imaging anomalies, the inward shrinkage anomaly and the outward extension anomaly. The imaging anomalies are detected around the corners and along the boundaries of the mask features with several anomaly detection templates. Virtual edges are adaptively generated according to the local imaging anomalies. The virtual edges are shifted to adjust the distribution of transparent regions on the mask and modify the local imaging anomalies. Several constraints and strategies are applied for efficient modifications and global control of the contour fidelity. In addition, the diffraction-limited property of the imaging system is fully utilized to separate the imaging evaluations at a coarse sampling level and the mask modifications at a fine sampling level, through the mask pixelation with two-phase sampling. It accelerates the imaging evaluations and guarantees the modification resolution as well. Simulations and comparisons demonstrate the superior modification efficiency of the proposed method.

Source mask optimization for extreme-ultraviolet lithography based on thick mask model and social learning particle swarm optimization algorithm.

Extreme ultraviolet (EUV) lithography plays a vital role in the advanced technology nodes of integrated circuits manufacturing. Source mask optimization (SMO) is a critical resolution enhancement technique (RET) or EUV lithography. In this paper, an SMO method for EUV lithography based on the thick mask model and social learning particle swarm optimization (SL-PSO) algorithm is proposed to improve the imaging quality. The thick mask model's parameters are pre-calculated and stored, then SL-PSO is utilized to optimize the source and mask. Rigorous electromagnetic simulation is then carried out to validate the optimization results. Besides, an initialization parameter of the mask optimization (MO) stage is tuned to increase the optimization efficiency and the optimized mask's manufacturability. Optimization is carried out with three target patterns. Results show that the pattern errors (PE) between the print image and target pattern are reduced by 94.7%, 76.9%, 80.6%, respectively.

Comparison of processing speed of typical wavefront reconstruction methods for lateral shearing interferometry.

Lateral shearing interferometry is widely applied in wavefront sensing, optical components testing, and defect inspection. The procedure of reconstructing the wavefront is the most specific difference between lateral shearing interferometry and other classical methods such as the Fizeau and Twyman interferometers. The speed and accuracy are two main features to evaluate the performance of one wavefront reconstruction method. In this work, optimized procedures for three typical wavefront reconstruction methods-the iterative FFT wavefront reconstruction method (FFT method), the partial differential least-squares method (LSQ method), and the difference Zernike polynomial fitting method (DZF method)-are designed. The calculation speeds of the three wavefront reconstruction methods are evaluated with different GPUs and CPUs. According to the test results, the DZF method is the fastest method both in the GPUs and CPUs. The shortest processing times of the DZF, FFT, and LSQ methods are 100, 449, and 494 ms, respectively, with the wavefront size of 1024×1024pixels. The calculation speeds of the FFT method and the LSQ method are similar in the CPUs, and the FFT method is faster in the GPUs. The relationship between the consumed time and the wavefront size is an exponential function in the CPUs and a power function in the GPUs. Generally speaking, GPUs' processing speeds are faster than CPUs'. But CPUs can be faster than GPUs when the test wavefront sizes are smaller than 64×64pixels. Besides, the differences between the consumed times of different CPUs are relatively smaller than those of the GPUs.