Yanqiu Li

Using the illuminator for high numerical aperture (NA) extreme ultraviolet (EUV) exposure tool in EUV lithography can lead to support volume production of sub-2 nm logic nodes and leading-edge DRAM nodes. However, the typical design method of the illuminator has issues with the transmission owing to the limitation of optical structure that cannot further reduce process parameter k1, and uniformity due to the restriction of matching method that can only consider one factor affecting uniformity. The all-facet illuminator can improve transmission by removing relay system. Deep reinforcement learning (RL) can improve the uniformity by considering multiple factors. In this paper, a design method of the all-facet illuminator for high NA EUV lithography exposure tool and a matching method based on deep RL for the double facets are proposed. The all-facet illuminator is designed using matrix optics, and removing relay system to achieve high transmission. The double facets is matched using the deep RL framework, which includes the policy network with improved trainability and low computational demands, and the reward function with great optimization direction and fast convergence rate, enabling to rapidly generate multiple matching results with high uniformity. An all-facet illuminator for a 0.55 NA EUV lithography exposure tool is designed by the proposed method. Simulation results indicate that the transmission is greater than 35%, and uniformity exceed 99% under multiple illumination pupil shapes.

The aberrations of the lithography objective are unevenly distributed at different field points. Full-field source and mask optimization (FFSMO) can reduce the impact of uneven aberration on projection lithography imaging. However, gradient conflicts between field points would prevent FFSMO merit function from converging to an optimal solution. In this paper, the gradient surgery (GS) theory is applied to solve the gradient conflict problem in FFSMO. To mitigate the gradient conflict, the proposed GS-FFSMO method projects the gradient of each field point onto the gradient normal plane of any other field point that has a conflicting gradient. Then, the source and mask patterns based on the corrected gradient information are updated. Compared with traditional FFSMO, the proposed method can effectively improve the lithography imaging fidelity and uniformity at full exposure field.

The Stokes-Mueller imaging polarimeter has garnered significant interest from various industries, including material characterization, chemical engineering, pharmaceuticals, biological science, and semiconductor production. The polarimeter should be capable of automatic detection to satisfy the requirements of practical applications. A graphical user interface-based automatic Stokes-Mueller imaging polarimeter detection method is suggested. The system automatically rotates the wave plate by the DC servo motor in accordance with predefined rotation angles, captures the output image, and then performs data processing such as polar decomposition, Mueller matrix solution, and calibrationgraphical user interface experimentation with picture presentation. The Stokes-Mueller imaging polarimeter automatic detection system's instrument, working principle, and software architecture are explained. The entire detection procedure is automated, which increases productivity. The automatic detection of air and biological samples demonstrated a 20% increase in work efficiency while keeping the instrument's high-resolution (NA=0.95) and high-accuracy (error≤0.24%) performance criteria.

Fast source optimization (SO) is an urgent need for 14-5 nm node integrated lithography online technology. Our earlier contour sample-Bayesian compressed sensing SO (CCS-BCS-SO) method has good image fidelity but is slow. In this paper, we propose what we believe to be a new certain interval contour sampling fast iterative shrinkage-thresholding algorithm compressed sensing SO (CICS-FISTA-SO) method. The fast iterative shrinkage-thresholding algorithm (FISTA) is accelerated by using the famous gradient acceleration strategy Nesterov acceleration technology to achieve both fast SO and high-fidelity imaging. The CICS-FISTA-SO method samples the image pixels on the contour region more evenly, preserves the image information more, and significantly reduces the computational complexity. To our best knowledge, FISTA technology is for the first time applied to resolution enhancement technology in a lithography system to ensure fast high-fidelity modes. The proposed CICS-FISTA-SO framework expresses the SO process as a series of reweighted l₁-norm problems, which can obtain a pattern of high image fidelity with fewer sampling measurements. The CICS-FISTA-SO method is 2.23 times faster than the CCS-BCS-SO method. In addition, the average mode error of the CICS-FISTA-SO method is 3.5% lower than that of the CCS-BCS-SO method. The results show that CICS-FISTA-SO can realize fast SO and high-fidelity imaging better than CCS-BCS-SO.

The advanced technology node has an exact error tolerance. The effect of projection objective aberration on imaging cannot be neglected. In order to reduce the effect of aberration, considering the central obscuration of the high-NA extreme ultraviolet projection objective, we propose a Tatian polynomial-based annular pupil wavefront optimization (TBAPWO) method. An active pupil consisting of 64-term Tatian polynomials is incorporated into the imaging model. The suitable coefficients of the Tatian polynomials are solved using the gradient iteration method. The optimized active pupil can improve lithographic imaging quality. Simulation results show that the TBAPWO method can effectively reduce the pattern error of imaging by more than 30%, which indicates that the effect of aberration on imaging is controlled.

Source and mask optimization (SMO) is an important lithography resolution enhancement technique for 22 nm technology nodes and beyond in lithography. This technique generates freeform sources by adjusting the inclination angle of the micromirror array (MMA). In this paper, a high efficiency regulation method to generate an illumination source by adopting the micromirror array in DUV lithography is proposed. The number and inclination angles of micromirrors required for adjustment to convert the initial source into any illumination source are quickly and accurately obtained by using the proposed method. The illumination source is obtained on the pupil plane by adjusting the inclination angles of some micromirrors. The simulation results show that the initial source is converted into another illumination source with different complexities by adjusting some micromirrors. Compared with the traditional MMA allocation method, when the initial source is the freeform source, the regulation efficiency of micromirrors has been improved by at least 20%. The method reduces the regulation time of micromirrors and the number of micromirrors required for adjustment. Moreover, the number of micromirrors required for adjustment is positively correlated with the coherence coefficient. The results illustrate that this method is effective and has significant engineering implications.

Identification of cervical intraepithelial neoplasia (CIN) tissues holds significant clinical importance in reducing the incidence and mortality of cervical cancer. The ultraviolet Mueller matrix imaging polarimeter (UV-MMIP) can significantly enhance morphological specific contrast. For the first time, to our best knowledge, we used polarimetric metrics measured by UV-MMIP combined with machine-learning techniques to achieve high-accuracy CIN classification. Initially, eight classifiers of four types were trained, and the best-performing one was identified, achieving a classification accuracy (F1 score) of up to 0.815. Subsequently, the best classifier from each type was assembled to construct a stacked model to further explore the potential of the machine-learning techniques, resulting in an improved F1 score of 0.838. Additionally, the classification results indicate that depolarization-related metrics have stronger discriminatory power on the trained classifier’s outcome than retardance-related metrics, which highlights the contribution of UV-MMIP for the classification task. Our work validates the feasibility of the polarization-based machine-learning framework for effective CIN classification.

Multilateral shearing interferometers (multi-LSIs) utilize the phase difference information of multiple shear directions with high accuracy and strong noise resistance. However, the interferogram fringe contrast of multi-LSIs can reverse due to the effect of contrast modulation, leading to incorrect measurement result. This issue has lacked comprehensive quantitative research to effectively guide the elimination of its effects. In this work, a quantitative analysis of the contrast modulation is proposed. This paper rigorously derives contrast modulation function, analyzing the effect of aberration and shear ratio on it. It is the first time that a quantitative theoretical model of aberration measurement range and shear ratio has been established, offering guidance in practical application for precise shear ratio adjustment to eliminate modulation effect and extend measurement range. By eliminating modulation effects, phase difference information from multiple directions can be effectively utilized, maximizing the benefits of multi-LSIs. In simulation and experiment, the distribution of fringe contrast reversal bands and the variation of aberration measurement range with shear ratio are consistent with theoretical analysis, which verifies the validity of the proposed approach. This approach aims to address the issue of interferogram fringe contrast inversion caused by modulation in multi-LSIs, providing a theoretical basis and practical guidance for precise measurement.

The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope–curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope–curvature information alone. Compared with the hybrid multi-directional slope–curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.

Local phase retardation (LPR) is increasingly recognized as a crucial biomarker for assessing disease progression. However, the presence of speckle noise significantly challenges its accuracy and polarization contrast. To address this challenge, we propose a signal-processing strategy aimed at reducing the impact of noise on LPR measurements. In this approach, the LPR is reconstructed by polar decomposition after averaging multiple Mueller matrices from different overlapping sub-spectra. To optimize measurement accuracy, we systematically combined and traversed different sub-spectral numbers and bandwidths. By examining the quarter-wave plate and glass slide, high-accuracy phase retardation measurements were successfully verified, and the maximum polarization contrast was improved by 23%. Moreover, experimental results from multi-tissue imaging vividly illustrate that the equivalent number of looks (ENL) and polarization contrast were improved by 18% and 19%, respectively. This outcome indicates that our proposed strategy can effectively reduce the noise spikes, enhancing tissue discrimination capabilities.