Holistic alignment approach for on-product overlay improvement on DUV lithography process with combined solutions

Ma, Jigang; Yu, Miao; Lambregts, Cees; Tsiachris, Sotirios; Böcker, Paul; Kim, Jun-Yeob; Ma, Won-Kwang; Han, Sangjun; Park, Chanha; Kim, Kyong-Seok; Kim, Junghwan; Park, Sangjun; Kim, Gwang-Gon

doi:10.1117/12.2552938

Cited by 3 publications

(6 citation statements)

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“…Similarly, by substituting Equation ( 16) into Equation (11), with N groups of asymmetric alignment marks and the corresponding multi-channel alignment position error data and least square method, the matrix expression can be obtained.…”

Section: Theoretical Modelmentioning

confidence: 99%

“…On-product overlay control is guaranteed by controlling alignment and overlay metrology, typically through feedforward and feedback control loops in customer fab [11]. The continuous reduction of alignment error is the key to ensuring the overlay accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The OCW method conducts the experiment with a limited number of wafers. In the real FAB, the various mark asymmetries are unpredictable, complicated and are mixed together [11,16,22]. The weight values with the optimal correction effect change with the mark asymmetry; thus, these fixed weighted methods have weaknesses in process robustness and flexibility.…”

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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Section: Theoretical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…With the continuous tightening of the overlay budget, the alignment accuracy is constantly improved under the circumstances of collaborative and holistic optimization so as to meet the requirements of overlay 33 . Main improvements in the alignment system are introduced in this section.…”

Section: Improvements In Alignment Systemmentioning

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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“…To meet a tighter overlay specification with the continuous shrinkage of dimensions on semiconductor devices, advanced scanners` alignment-related options and improved overlay process control methods have been adopted by lithographers. For example, ASML scanners provide higher-order wafer alignment models, such as HOWA3 and radial basis function model (RBF), to reduce high-order wafer grid distortion, and optimal color weighting (OCW) to minimize the impact of alignment mark deformation and thus improve wafer-to-wafer variation [1]. Manufacturers also used feed-forward methods, trying to take the process correction values or optimal correction values from a previous layer to a current layer, to minimize lot-to-lot variation on the current layer [2].…”

Section: Introductionmentioning

confidence: 99%

Advanced cross-layer overlay simulation with exposure grid re-calculation

Lu,

Tsou,

Demirer

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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In today's advanced semiconductor industry with the multitude of new products and device requirements, lithography process changes are inevitable and expected due to tighter overlay specifications and the increasing influence of process effects. To minimize the overlay impact from the wafer-to-wafer and lot-to-lot variation, lithography engineers often need to adjust wafer alignment and overlay strategies to optimize product yield. Overlay simulation methods are often used to estimate if new alignment and/or overlay strategies can help achieve better overlay performance. By removing the old corrections from the original alignment and overlay data, and then applying new corrections, the overlay performance can be readily predicted, saving significant time. However, available software suites only simulate the current layer overlay. Overlay risks would therefore remain on subsequent layers without an effective simulation method to predict the impact resulting from changes to the alignment and/or overlay strategies on the current layer.In our study, we demonstrate a new method to simulate the overlay performance on the subsequent layers by re-calculating the current layer exposure grid. Our dataset has 3 layers: L0, L1, and L2. On L1 (current layer), we apply a new alignment model and overlay control strategy with a feedforward solution. Then, we re-calculate the exposure grid to generate a new (virtual) alignment and overlay dataset for L2 (following layer). Finally, we estimate the overlay impact on L2 with the new alignment and overlay dataset and summarize the benefits of this new cross-layer overlay simulation method.

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Holistic alignment approach for on-product overlay improvement on DUV lithography process with combined solutions

Cited by 3 publications

References 0 publications

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

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

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

Advanced cross-layer overlay simulation with exposure grid re-calculation

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