Effectiveness of Electrical and Optical Detection at Pixel Circuit on Thin-Film Transistors

Tzu, Fu-Ming

doi:10.3390/mi12020135

Cited by 4 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the output voltage is two times higher, as shown in Figure 7 ; Figure 7 A illustrates the array tester, Figure 7 B indicates the probing on the glass at the short circuit, and Figure 7 C illustrates two times the output voltage, as indicated by the red circle. Thirdly, in the cross short defect states, the sensor receives a higher voltage than the predicted output, and therefore the output voltage is more elevated, as shown in Figure 8 ; Figure 8 A illustrates the array tester at the cross short, Figure 8 B indicates the probing on the glass at the cross short circuit, and Figure 8 C indicates a voltage tendency of the sensor, shown as a red circle in the sensor numbers at 3~4 [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

Detection of Electrical Circuit in a Thin-Film-Transistor Liquid-Crystal Display Using a Hybrid Optoelectronic Apparatus: An Array Tester and Automatic Optical Inspection

2021

Self Cite

View full text Add to dashboard Cite

In this study, we developed a high-resolution, more accurate, non-destructive apparatus for refining the detection of electrode pixels in a thin-film-transistor liquid-crystal display (TFT-LCD). The hybrid optoelectronic apparatus simultaneously uses an array tester linked with the automatic optical inspection of panel defects. Unfortunately, due to a tiny air gap in the electro-optical inspector, the situation repeatedly causes numerous scratches and damages to the modulator; therefore, developing alternative equipment is necessary. Typically, in TFT-LCDs, there are open, short, and cross short electrical defects. The experiment utilized a multiple-line scan with the time delay integration (TDI) of a charge-coupled device (CCD) to capture a sharp image, even under low light, various speeds, or extreme conditions. In addition, we explored the experimental efficacy of detecting the electrode pixel of the samples and evaluated the effectiveness of a 7-inch opaque quartz mask. The results show that an array tester and AOI can detect a TFT-LCD electrode pixel sufficiently; therefore, we recommend adopting the hybrid apparatus in the TFT-LCD industry.

show abstract

Section: Resultsmentioning

confidence: 99%

Detection of Electrical Circuit in a Thin-Film-Transistor Liquid-Crystal Display Using a Hybrid Optoelectronic Apparatus: An Array Tester and Automatic Optical Inspection

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The power industry is always a strategic pillar industry for national economy. In the grand background of economic development, the power industry is constantly upgraded in an intelligent manner, and the inspection meters of electric circuit are playing an increasingly important role in the stable operation of power systems [1][2][3][4][5][6][7]. Due to the effect of multiple factors, the measuring meters need to be inspected regularly, and the installation environment of these meters is often complex and ever-changing [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Recognizing Readings in Images of Electric Circuit Inspection Meters Based on Deep Learning

Xia¹,

Zhang²,

Zheng³

et al. 2023

View full text Add to dashboard Cite

For electric circuit inspection, the conventional manual inspection method has a series of problems including the heavy work load of recording the readings, the low accuracy, and the hidden safety hazards. The intelligent recognization method for images of meter readings based on digital image technology has a great practical value. However, the existing recognization methods for meter readings based on deep learning generally ignore the extraction of key points such as the pointer and scale on meter dashboard, the existing algorithms are of poor robustness and anti-jamming ability, therefore, this paper aims to study a novel method for recognizing readings in the images of electric circuit inspection meters based on deep learning. At first, this paper corrects the tilt of meter dashboard, and accurately positions the dashboard center. Then, based on the YOLOv5 network model, this paper constructs the said recognization model, gives the structure of the YOLOv5 network model, and introduces its working principle. At last, experimental results are drawn to verify the validity of the proposed method for processing the images of meter readings and the constructed recognization model.

show abstract

“…In this process, the efficiency of the image preprocessing and image classification determines the efficiency of the entire process. Many studies have discussed topics related to the assistance of AOI in decision-making [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multicategory Defect Detection Method Based on the Convolutional Neural Network Method for TFT-LCD Panels

Chang

Meng

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Defects on thin film transistor liquid crystal display (TFT-LCD) panel could be divided into either macro- or microdefects, depending on if they are easy to be detected by the naked eye or not. There have been abundant studies discussing the identification of macrodefects but very few on microones. This study proposed a multicategory classification model using a convolutional neural network model to work with automatic optical inspection (AOI) for identifying defective pixels on the TFT-LCD panel. Since the number of nondefective pixels outnumbered the defective ones, there exists a very serious class-imbalanced problem. To deal with that, this study designed a special training strategy that worked with data augmentation to increase the effectiveness of the proposed model. Actual panel images provided by a mobile manufacturer in Taiwan are used to demonstrate the efficiency and effectiveness of the proposed approach. After validation, the model constructed by this study had 98.9% total prediction accuracy and excellent specificity and sensitivity. The model could finish the detection and classification process automatically to replace the human inspection.

show abstract

Effectiveness of Electrical and Optical Detection at Pixel Circuit on Thin-Film Transistors

Cited by 4 publications

References 25 publications

Detection of Electrical Circuit in a Thin-Film-Transistor Liquid-Crystal Display Using a Hybrid Optoelectronic Apparatus: An Array Tester and Automatic Optical Inspection

Detection of Electrical Circuit in a Thin-Film-Transistor Liquid-Crystal Display Using a Hybrid Optoelectronic Apparatus: An Array Tester and Automatic Optical Inspection

A Novel Method for Recognizing Readings in Images of Electric Circuit Inspection Meters Based on Deep Learning

A Novel Multicategory Defect Detection Method Based on the Convolutional Neural Network Method for TFT-LCD Panels

Contact Info

Product

Resources

About