Efficient and improved qualification method for patterns with irregular edges in printed electronics

Liu, Ting-Jeng; Hsu, Steve Lien Chung; Wu, Meng-Jhu; Ianko, P. E.; Lo, Cheng‐Yao

doi:10.1088/1361-6439/ab4ed7

Cited by 5 publications

(10 citation statements)

References 28 publications

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“…AOI ML AI [4] Non-EM patterns (noticeable LER tolerance) Yes No No [13] Non-EM pattern (improved LER tolerance) Yes No No [14] EM pattern (improved LER efficiency with one-arm distortion) Yes No No [15] EM pattern (improved LER efficiency with one-arm distortion) Yes Yes (passive signal) Yes (passive signal) This work EM pattern (improved LER efficiency with two-arm distortion) Yes Yes (active signal) Yes (active signal) aforementioned activities are considered an earlywarning system and in case the characteristics deviated from the expected values, actions such as rework or scrap can be made to save time and suppress further cost. However, before Metal-1, plenty time and cost have already been spent, thus engineers also consider other possible examination methods.…”

Section: Supportivenessmentioning

confidence: 99%

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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%

“…All 144 samples in the training group went through identical LER analysis procedures [14]. The LERs of the two contours on both arms of one SCASA were collected.…”

Section: Ler Analysismentioning

confidence: 99%

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

Wu¹,

Chang²,

Chung³

et al. 2022

Flex. Print. Electron.

Self Cite

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“…To ensure the fabrication quality of PCBs, researchers have proposed various methods to quantify pattern integrity by evaluating their visual appearance using automatic optical inspection (AOI) procedures [ 1 , 2 ]. Line width roughness (LWR) [ 3 ] and line edge roughness (LER) [ 4 ] have been adopted, which facilitate the evaluation with scientific and quantified indicators. Process instability can thus be determined or suspected by identifying geometric changes [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

Qiu,

Tsai,

Chen

et al. 2024

Sensors

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An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

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“…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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Efficient and improved qualification method for patterns with irregular edges in printed electronics

Cited by 5 publications

References 28 publications

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

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

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

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

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