Enhacement of intrafield overlay using a design based metrology system

Jo, Gyoyeon; Ji, Sunkeun; Kim, Shinyoung; Kang, Hyunwoo; Park, Minwoo; Kim, Sang-Woo; Kim, Jungchan; Park, Chanha; Yang, Hyunjo; Maruyama, Kotaro; Park, Byung‐Jun

doi:10.1117/12.2218937

Cited by 2 publications

(5 citation statements)

References 3 publications

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“…Similar to Scenario 1, both the inverse and forward models were built at three wavelengths. For Model 1 (Model 2), the inverse model achieved good approximation ability when the number of hidden layer neurons was greater than 21 (8), 6 (10), and 10 (8) at 200 nm, 400 nm, and 600 nm, respectively. Then, the forward model was constructed and applied.…”

Section: Results and Discussion For Scenario 2 531 Validation Of The ...mentioning

confidence: 99%

“…For example, the etching and chemical mechanical polish process leads to geometric features such as the sidewall angle (SWA), top rounded angle, and the top angle, as illustrated in figures 1(b)-(d). Both two processes can lead to inaccurate measurement signals and ultimately unacceptable OVL metrology errors [8][9][10][11]. Therefore, it is necessary and critical to investigate the impacts of the target defects on the DBO metrology performance, particularly the metrology accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Calculate the PAWN index S i,j|Qi by the operator δ 12. Update the Nu, Nc values and repeat steps(6)(7)(8)(9)(10)(11) until the condition satisfies the threshold τ to obtain the convergent sensitivity index S i,j|Qi . 13.…”

mentioning

confidence: 99%

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Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

Wang,

Meng,

Zhang

et al. 2024

Meas. Sci. Technol.

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Overlay (OVL) is one significant performance indicator for the lithography process control in semiconductor manufacturing. The accuracy of the OVL metrology is extremely critical for guarantee the lithography quality. Currently, diffraction-based overlay (DBO) is one of the mainstream OVL metrology techniques. Unfortunately, the accuracy of the DBO metrology is largely affected by the defect features of the overlay target. Therefore, there is a strong need to investigate the impacts of these target defects on the DBO metrology performance. However, efficiently investigating the statistical and interactive impacts of various DBO target defects remains challenging. This study aims to address this issue through proposing an intelligent sensitivity analysis approach. A cumulative distribution based Global Sensitivity Analysis (GSA) method is utilized to assess the nonlinear influences of multiple defects in the OVL target on the DBO inaccuracy. The scenarios with both known and unknow distributions of the OVL target defects are considered. For the former, a neural network driven forward model is constructed for fast calculating the optical diffraction responses to accelerate the GSA process. For the latter, another neural network based inverse model are built for efficiently estimating the distribution of the target defects. Finally, a series of simulation experiments are conduct for typical DBO targets with multiple common defect features. The results demonstrate the effectiveness and robustness of the proposed approach as well as give valuable insights into the DBO defect analysis. Our study provides a strong tool to assist the practitioners in achieving intelligent and efficient DBO analysis and thus in enhancing OVL metrology performance.

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Section: Results and Discussion For Scenario 2 531 Validation Of The ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

Wang,

Meng,

Zhang

et al. 2024

Meas. Sci. Technol.

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“…One of the most important issues for improving the fabrication yield of semiconductor devices is to control layer-tolayer overlay, which makes it necessary to measure the overlay of the actual circuit pattern accurately. Due to design rule shrinkage of semiconductor devices, an optical-based measurement method for a dedicated overlay mark is no longer able to represent the exact circuit-level overlay [1]. The overlay of actual circuit patterns therefore needs to be measured by using a critical dimension scanning electron microscope (CD-SEM) [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Due to design rule shrinkage of semiconductor devices, an optical-based measurement method for a dedicated overlay mark is no longer able to represent the exact circuit-level overlay [1]. The overlay of actual circuit patterns therefore needs to be measured by using a critical dimension scanning electron microscope (CD-SEM) [1,2]. In cases where the actual circuit patterns of a measurement target consist of upper layer patterns and buried lower layer patterns, an overlay measurement method using both secondary electrons (SE) images and backscattered electrons (BSE) images obtained from a high voltage CD-SEM (HV-SEM) has been proposed [3].…”

Section: Introductionmentioning

confidence: 99%

A method for dynamic placement of overlay measurement area cursors using segmentation technique

Kyogoku,

Harada,

Osaki

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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As the design rules for semiconductor devices continue to shrink, layer-to-layer overlay of actual circuit patterns needs to be measured by using a critical dimension scanning electron microscope (CD-SEM). In addition to the overlay, process variations are making not only local size variations but also local position errors in patterns on the same layer increasingly non-negligible compared to pattern size. In cases where upper and lower patterns partially overlap, these variations and errors cause variations to occur in the positions and visible area of the lower layer patterns in SEM images. As a result, the center position of each lower layer pattern has become difficult to accurately measure resulting in errors in the overlay measurement. In response to this problem, we have developed a method for dynamically placing measurement area cursors for overlay metrology using segmentation technique. This method determines a measurement area cursor by recognizing the position and size of each lower layer pattern through segmentation images generated from the target SEM images using unsupervised deep learning. The performance of this method is evaluated by creating imitated images having the programmed overlay, the local size variations, and the local position errors. We compared the measurement results for the imitated images obtained by the proposed method and a conventional method that uses fixed measurement area cursors and found that the proposed method maintains sensitivity and repeatability even for targets where sensitivity and repeatability degrade with the conventional method due to process variations.

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Enhacement of intrafield overlay using a design based metrology system

Cited by 2 publications

References 3 publications

Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

A method for dynamic placement of overlay measurement area cursors using segmentation technique

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