Computational scanner wafer mark alignment

Menchtchikov, Boris; Socha, Robert; Raghunathan, Sudharshanan; Lyulina, Irina; Schoonewelle, Hielke; Tinnemans, Patrick; Tuffy, Paul; Leray, Philippe; Jehoul, Christiane

doi:10.1117/12.2259750

Cited by 7 publications

(9 citation statements)

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“…The input parameters of the simulation system include the mark type, segmentation method, device pattern information, design rule and full stack information (such as material thickness, n and k). Through a series of simulation calculations, the corresponding KPIs will be the output as the evaluation of the alignment position deviation and reproducibility to help reduce the overlay contribution from alignment marks 41,44 .…”

Section: Development Of Alignment Marks and Efficient Methods For Mar...mentioning

confidence: 99%

“…For instance, asymmetry may be induced, the duty cycle may change and the phase depth of the alignment mark may be altered 45 . Among them the asymmetry causes a phase change of the diffraction beam, as shown in figure 10, and such phase change will induce the alignment position deviation (APD) and overlay error 44,46 .…”

Section: Optimal Color Weighting Algorithmmentioning

confidence: 99%

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Review of overlay error and controlling methods in alignment system for advanced lithography

Hao¹,

Qi²

2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

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Acting as one of the three critical indicators to evaluate the performance of lithography scanners, overlay has been a vital factor which seriously affects the electric property, life span and reliability of integrated circuits (IC). With continuous developments of technology nodes and applications of resolution enhancement technologies (RET), current resolution of the scanners has shrunk to 7 nm and beyond, proposing more stringent requirements for measuring, monitoring and correcting the overlay error. To ensure that the overlay budget fully met by the scanner, advanced optical technologies and holistic alignment approaches have been gradually applied to the alignment system of the scanner, revealing that an integrated methodology in which multiple technologies are adopted proved to be valid and effective in reducing the overlay error in present technology nodes.In this article, the exact concept of overlay error is provided, and on this basis different methods to describe the overlay error are comprehensively analyzed from the perspectives of classifications, sources, quality control and correcting models. Then the systematic relationship between alignment and overlay is illustrated. Recent improvements in the alignment system are also the key points here to systematically reduce the overlay error, including the optimization of the alignment optical path, mark types and design procedures, as well as the application of optimal color weighting (OCW), wafer alignment model mapping (WAMM) and sampling scheme optimization (SSO) algorithms. Based on the combined application and iteration of these technologies, it is believed that the on-product overlay (OPO) can be further compressed to support the continuous tightening of the overlay budget in the industry of IC.

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Section: Development Of Alignment Marks and Efficient Methods For Mar...mentioning

confidence: 99%

Section: Optimal Color Weighting Algorithmmentioning

confidence: 99%

Review of overlay error and controlling methods in alignment system for advanced lithography

Hao¹,

Qi²

2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

View full text Add to dashboard Cite

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“…The overlay error beyond the limitation may cause gross deviation of the IC components and devices, and lead to short circuits, open circuits and other problems [3][4][5]. According to the International Technology Roadmap for Semiconductors (ITRS) and other institutes, for process nodes of 10 nm and 7 nm, the overlay accuracy is required to be below 3 nm and 2 nm [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, several methods are employed to correct alignment position error, including the process verification method, multi-diffraction order-weighted method and multi-channel-weighted method. In the process verification method, the overlay error of the two layers is precisely measured using scanning electron microscopy (SEM) after lithography, and the alignment position error will be corrected in the subsequent process based on the measured overlay error [6,17,22]. However, since the mark asymmetries differ between wafers and exposure fields, even for the same batch and process, this method can not correct the alignment error stably.…”

Section: Introductionmentioning

confidence: 99%

“…However, since the mark asymmetries differ between wafers and exposure fields, even for the same batch and process, this method can not correct the alignment error stably. The process verification method is time-consuming, costly, destructive and lacks robustness [6,7]. This also indicates that it is not practical to obtain the asymmetry of the alignment mark directly before the alignment process.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment

Song,

Wang,

et al. 2024

Sensors

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As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels’ results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error’s Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

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