Robust hybrid source and mask optimization to lithography source blur and flare

Han, Chunying; Li, Yanqiu; Ma, Xu; Liu, Lihui

doi:10.1364/ao.54.005291

Cited by 23 publications

(3 citation statements)

References 34 publications

(35 reference statements)

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“…In this section, we develop a gradient-based SNPCO method to comprehensively optimize the source, NA, and process parameters for mitigating the impacts of mask absorber errors on lithographic imaging performance. For the statistical optimization problem described in Equations (10) and (11), the SGD algorithm is adept in solving such problem [26,27]. Thus, we use the SGD algorithm to optimize the source.…”

Section: Snpco Methods For Duv Lithography Systemmentioning

confidence: 99%

Mitigating the Impact of Mask Absorber Error on Lithographic Performance by Lithography System Holistic Optimization

Sheng

Sun

et al. 2019

Applied Sciences

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The non-ideal mask absorber can cause an increase in critical dimension error (CDE) and decrease in process window (PW). However, the random mask absorber errors induced during mask fabricating and measuring are not considered in computational lithography. The problem cannot be neglected as the continuous scaling of lithography technology node. In this work, for the first time to our knowledge, a source, numerical aperture (NA), and process parameters co-optimization (SNPCO) method is developed to reduce the CDE induced by absorber errors and improve the PW. First, the source is represented by Zernike polynomials to balance computational burden and flexibility of source. Then a weighted cost function containing CDE and PW that incorporates the influences of absorber errors is created. Finally, a statistical optimization method is used to optimize the lithographic system parameters. Simulations of 1D mask pattern show that for the system with extreme absorber errors, the pattern errors of the proposed method are reduced by 62.1% and 58.9%, and the PWs are increased by 40.3% and 36.4%, respectively. The results illustrate that this method is effective in mitigating the CDE caused absorber errors and improving process robustness.

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Section: Snpco Methods For Duv Lithography Systemmentioning

confidence: 99%

Mitigating the Impact of Mask Absorber Error on Lithographic Performance by Lithography System Holistic Optimization

Sheng

Sun

et al. 2019

Applied Sciences

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“…Table 1 illustrates the optimization flow of the SGD and MBGD algorithm, respectively. In our previous works [25,26], SGD was adopted to calculate the gradient in a single training sample β i,k in each iteration with fast speed. However, due to the wider sample range of defocus disturbances, the SGD algorithm could not guarantee each iteration was conducted in the global optimal direction.…”

Section: Drsmo Optimization Algorithmmentioning

confidence: 99%

“…We have previously used the SGD algorithm to solve multi-objective SMO [25,26] and it converged well with fast speed. However, due to the wider sampling range of defocusing in the DRSMO framework, it was hard for the SGD algorithm to search for the global optimal direction if each iteration was only driven by one sample gradient in the training set.…”

Section: Comparison Of Sgd and Mbgd Algorithm For Drsmomentioning

confidence: 99%

Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty

Wei

et al. 2019

Applied Sciences

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The continuous decrease in the size of lithographic technology nodes has led to the development of source and mask optimization (SMO) and also to the control of defocus becoming stringent in the actual lithography process. Due to multi-factor impact, defocusing is always changeable and uncertain in the real exposure process. But conventional SMO assumes the lithography system is ideal, which only compensates the optical proximity effect (OPE) in the best focus plane. Therefore, to solve the inverse lithography problem with more uniformity of pattern in different defocus variations, we proposed a defocus robust SMO (DRSMO) approach that is driven by a defocus sensitivity penalty function for the first time. This multi-objective optimization samples a wide range of defocus disturbances and it can be proceeded by the mini-batch gradient descent (MBGD) algorithm effectively. The simulation results showed that a more robust defocus source and mask can be designed through DRSMO optimization. The defocus sensitivity factor sβ maximally decreased 63.5% compared to conventional SMO, and due to the low error sensitivity and the depth of defocus (DOF), the process window (PW) was further enlarged effectively. Compared to conventional SMO, the exposure latitude (EL) maximally increased from 4.5% to 10.5% and DOF maximally increased 54.5% (EL = 5%), which proved the validity of the DRSMO method in improving the focusing performance.

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Informational Lithography Approach Based on Source and Mask Optimization

Pan

Zhang

et al. 2021

IEEE Trans. Comput. Imaging

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Robust hybrid source and mask optimization to lithography source blur and flare

Cited by 23 publications

References 34 publications

Mitigating the Impact of Mask Absorber Error on Lithographic Performance by Lithography System Holistic Optimization

Mitigating the Impact of Mask Absorber Error on Lithographic Performance by Lithography System Holistic Optimization

Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty

Informational Lithography Approach Based on Source and Mask Optimization

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