Linking EUV lithography line edge roughness and 16nm NAND memory performance

Pret, Alessandro Vaglio; Poliakov, Pavel; Gronheid, Roel; Blomme, Pieter; Corbalan, Miguel Miranda; Dehaene, Wim; Verkest, D.; Houdt, J. Van; Bianchi, Davide

doi:10.1016/j.mee.2012.04.013

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…To avoid misunderstanding of LWR and pattern integrity, line edge roughness (LER) was proposed [4] which was able to quantify the aforementioned pattern distortion on individual sides of lines. Although it is still impossible to obtain the electrical performance at these patterning stages, engineers tried to link the LWR or LER to various transistor characteristics with relationships that imply performance tendencies [5][6][7][8][9][10].…”

Section: Supportivenessmentioning

confidence: 99%

Electromagnetic characteristic estimation on spiral antennas through AOI, ML, and AI

Wu¹,

Chang²,

Chung³

et al. 2022

Flex. Print. Electron.

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In this study, a method that is able to estimate the electromagnetic characteristic of spiral antennas was proposed and realized through consecutive procedures of automatic optical inspection (AOI), machine learning (ML), and artificial intelligence (AI), providing a solution to smart manufacturing. Two-arm self-complementary Archimedean spiral antennas (SCASAs) were introduced as examination targets with pattern distortions from potential process variations, in which bulges and neckings were mathematically generated to imitate uncontrollable ink rheology in printed and flexible electronics, covering the unexplored parts in previous works. The SCASAs in the training group were fabricated by standard printed circuit board procedures, and their pattern integrity in terms of line edge roughness (LER) and coupling frequency were collected through AOI for ML as the feature and label, respectively. The established AI model was based on Gaussian process regression with covariance function of exponential that showed the smallest root-mean-square-error and the largest coefficient of determination through iterative lazy-learning. By feeding the LERs of the SCASAs into the testing group, their corresponding coupling frequencies were estimated by the established AI model with high confidence level. Good linearity between the estimated and measured responses indicated that a reliable AI model and procedure were built, which outperforms existing methods that are unable to project off-line active characteristics of microelectronic components from their in-line pattern integrities.

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Section: Supportivenessmentioning

confidence: 99%

Electromagnetic characteristic estimation on spiral antennas through AOI, ML, and AI

Wu¹,

Chang²,

Chung³

et al. 2022

Flex. Print. Electron.

View full text Add to dashboard Cite

show abstract

“…Although literature has indicated potential relationships between pattern distortion and its EM performance [9][10][11][12][13], they failed to provide solutions to predict the results. Researchers also tried to establish related system and procedures to predict some responses of Archimedean spiral antennas through their fabrication features, results were incomplete due to impractical target design, non-optimized software and hardware modules, insufficient data, low confidence level of results, and non-integrated humanmachine interface (table 1) [14,15].…”

Section: Introductionmentioning

confidence: 99%

Machine-learning based characteristic estimation method in printed circuit board production lines

Tsai

Qiu

et al. 2023

Flex. Print. Electron.

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In this study, software and hardware that supported automatic optical inspection (AOI) for printed circuit board production line was proposed and demonstrated. The proposed method showed an effective solution that predicts off-line electromagnetic (EM) characteristic of manufactured components through in-line pattern integrity. A spiral antenna that represented complex patterns was used as the evaluation target with imitated production variations. Numerical evaluation on EM properties, batch fabrication, hardware setup and optimization, algorithm and graphical user interface development, machine learning and artificial intelligence modeling, and data verification and analysis were thoroughly conducted in this study. Results indicated that when the antenna showed pattern distortion, its passive capacitance, active intensity, and active frequency increased, decreased, and decreased, respectively. These results proved that the developed system and method overcame the inability of in-line EM measurement in conventional setup. The results also showed high estimation accuracy that was not yet achieved in the past. Compared to existing or similar AOI ideas, the proposed method supports analyses on complex pattern, provides solutions on target design, and efficient algorithm generation. This work also proved active and passive EM signals with evidences, and exhibited outstanding confidence levels for characteristic estimations. The proposed system and method indicated their potential in smart manufacturing.

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“…Furthermore, the high demand for microelectronics and nanoelectronic devices increases because they are highly-precious and used to control dimensional structures in wafer-scale manufacturing [17][18][19][20]. The device invention methodologies are already well known in electronic manufacture using silicon chips miniaturization on a large scale [21][22].…”

Section: Introductionmentioning

confidence: 99%

Improving the conductivity and flexibility of chips fabricated from a copolymer PDMS-PEI and graphene assembled with nano metals

Abdelrahman

Erchiqui

Nedil

2021

Preprint

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Our work aims to make a unique polymer to be used as a conductive and flexible chip antenna. Its properties are robustness, rigidity, stretchability, and good conduction. The fabricated composite is composed of two copolymers, PDMS and PEI, assembled with nano metals (Cu, Ag), and graphene nanoparticles as a matrix. Nano metals fill out the inter-layer space, and polymer voids reinforce the crosslinker. Graphene/metal nanoparticles help make chelating complexes using metallic bonds, enhancing the polymer’s conductivity from 1.87* 10− 4 to 5.64*10− 6 σ Scm− 1. We analyze the conductivity, self-healing, and surface morphology of fabricated composite using different spectroscopic techniques, such as electrochemical impedance (EIS), SEM, TEM, IR, UV, and a particle size analyzer.

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Linking EUV lithography line edge roughness and 16nm NAND memory performance

Cited by 13 publications

References 17 publications

Electromagnetic characteristic estimation on spiral antennas through AOI, ML, and AI

Electromagnetic characteristic estimation on spiral antennas through AOI, ML, and AI

Machine-learning based characteristic estimation method in printed circuit board production lines

Improving the conductivity and flexibility of chips fabricated from a copolymer PDMS-PEI and graphene assembled with nano metals

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