Sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation for wafer alignment

Pan, Songyong; Wang, Shaoqing; Xu, Jing; Fan, Lili; Yuan, Fenghua; Shu, Ting; Dai, Fengzhao; Yan, Xiaona; Yang, Bojun; Wang, Xiangzhao

doi:10.1364/ao.437440

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Dozens of alignment marks are distributed on the entire wafer, and each alignment mark's position is measured by an alignment sensor to obtain wafer distortion. According to the wafer distortion, the exposure process parameters are adjusted to accurately transfer the circuit patterns from mask to wafer [12,13]. The overlay accuracy is generally 1/3 that of the process nodes, and the alignment accuracy is about 1/5~1/3 of the overlay accuracy.…”

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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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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“…Many methods can obtain sub-pixel level position information. There are mainly methods based on Zernike moments and edge gradient interpolation 5 ; Methods based on edge fitting, including polynomial fitting 6 , and Gaussian function fitting 7 ; And the method based on interpolation 8 . In addition, there are many algorithms based on the gray center of gravity method 9 , including the binary center of gravity method, the gray center of gravity method and the gray square center of gravity method.…”

Section: Introductionmentioning

confidence: 99%

Machine-vision-based automatic levels compensation error verification

Wang

2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2022)

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Algorithms for locating the center position of the crosshair image are studied, and a method based on machine vision to verify the compensation error of automatic levels is proposed. An illumination device is added behind the reticle of the automatic level. The reticle image is focused to infinity, imaged on the focal plane of the lens of the collimator, and images are captured using a CCD camera. Moreover, the necessary processing is performed to locate the center position of the crosshair image by the gray squared center of gravity method and the iterative weighted fitting method to obtain the subpixel level position coordinates. The metrological verification of the compensation error is completed after calibrating the angular grid values of the CCD in the x and y directions, the error angles of the x and y axes, and the parameters of the collimator coordinate system and attitude. The experimental results show that the machine vision-based method meets the requirements of the metrological verification of the compensation error of automatic levels, and the accuracy of the measurement results is higher than the traditional optical method.

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“…Its effect increases when the semiconductor process enters the era of deep submicron meter (below 20 nm), e.g., EUV process. In order to solve the alignment problem, there are several approaches [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The most typical device is wafer pre-alignment system (which is called pre-aligner) that consists of rotation mechanics and optical vision sensor [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Wafer Center Alignment System of Transfer Robot Based on Reduced Number of Sensors

Kim¹

2022

Sensors

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This brief presents a wafer alignment algorithm with reduced sensor number that obtains the relative distance of the wafer center and the robot hand. By `reduced number’, in spite of smaller number of sensors than the conventional method, we mean an improved method which achieves the similar results to pre-existing algorithms. Indeed, it can be designed with only three sensor data, less than four sensors of the conventional algorithm. Thus, some advantages of the proposed alignment algorithm include that it can be designed with low cost and less computing power. The proposed alignment algorithm is applied to a transfer robot for coater/developer system in semiconductor processing for verifying the performance of the method. The performance of the proposed method is validated by both simulation and experimental results.

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Sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation for wafer alignment

Cited by 6 publications

References 21 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

Machine-vision-based automatic levels compensation error verification

Wafer Center Alignment System of Transfer Robot Based on Reduced Number of Sensors

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