Holistic feedforward control for the 5 nm logic node and beyond

Megens, Henry; Brinkhof, Ralph; Aarts, Igor; Kok, H.; Karssemeijer, Leendertjan; Haaf, G. ten; Lee, Shawn; Slotboom, Daan; Ruiter, Chris de; Lyulina, Irina; Huisman, Simon; Keij, Stefan; Mos, E.C.; Tel, Wim; Rijpstra, M.; Schmitt-Weaver, Emil; Bhattacharyya, Kaustuve; Socha, Robert; Menchtchikov, Boris; Kubis, Michael; Mulkens, Jan

doi:10.1117/12.2515449

Cited by 4 publications

(2 citation statements)

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“…And such predictions are verified by simulation. Furthermore, WAMM can be combined with OCW to further reduce the process impact on alignment marks for HVM production and the overlay variations on the whole wafer 13,48 .…”

Section: Wafer Alignment Model Mapping 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)

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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: Wafer Alignment Model Mapping 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)

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“…As a result of the increasing application of new materials in lithography, the alignment sensor has developed from single-wavelength to multi-wavelength, and then to multi-wavelength and multi-polarization state [29][30][31]. In 2017, ASML released the ORION alignment sensor on the NXT2000i lithography machine.…”

Section: Introductionmentioning

confidence: 99%

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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Using Feedforward in on-Product Overlay Run-to-Run Control Loop for Reducing Lot-to-Lot Variation for a MEOL Layer of an Advanced Logic Node

Yang

Zhang

Huang

et al. 2021

2021 International Workshop on Advanced Patterning Solutions (IWAPS)

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Holistic feedforward control for the 5 nm logic node and beyond

Cited by 4 publications

References 0 publications

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

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

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

Using Feedforward in on-Product Overlay Run-to-Run Control Loop for Reducing Lot-to-Lot Variation for a MEOL Layer of an Advanced Logic Node

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