Electron-beam testing of flat panel display substrates

Schmitt, R.; Brünner, Matthias; Winkler, D.

doi:10.1016/0167-9317(94)90052-3

Cited by 9 publications

(4 citation statements)

References 1 publication

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“…Currently, optical inspection [4][5][6] , voltage imaging optical system (VIOS) [7][8][9] , electron beam [10][11] , direct probing [12][13][14] , and one-dimensional capacitance sensor [15][16] are prevalent in inspecting thin film transistor (TFT) backplane substrates. However, an optical inspection cannot detect electrical defects and has the disadvantage of frequent over-detection.…”

Section: Introductionmentioning

confidence: 99%

[Paper] High-Resolution Defect Detection for Flat Panel Display Using Proximity Capacitance Image Sensor

Yasuda,

Sugawa,

Kuroda

et al. 2023

MTA

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Section: Introductionmentioning

confidence: 99%

[Paper] High-Resolution Defect Detection for Flat Panel Display Using Proximity Capacitance Image Sensor

Yasuda,

Sugawa,

Kuroda

et al. 2023

MTA

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“…Most of the defects of all the four main types occur in the front‐end process. From the point of view of IPQC (In Process Quality Control), which is essential for the productivity of display manufacturing, point‐defects, line‐defects, and circuit‐defects can be mostly detected by automatic in‐line tests in the front‐end process with prompt feedbacks to improve the manufacturing process 1–6 …”

Section: Introductionmentioning

confidence: 99%

“…From the point of view of IPQC (In Process Quality Control), which is essential for the productivity of display manufacturing, point-defects, line-defects, and circuit-defects can be mostly detected by automatic in-line tests in the frontend process with prompt feedbacks to improve the manufacturing process. [1][2][3][4][5][6] However, automatic detection of mura is exceptionally difficult, as pointed out by many previous works, and thus, most manufacturing lines detect mura on the fabricated displays by the human visible test in the back-end process. [7][8][9][10][11] This human-based test causes problems such as inconsistent test accuracy, high labor cost, and, most important, the delay of feedback to the manufacturing process resulting in an impact on the productivity.…”

Section: Introductionmentioning

confidence: 99%

In‐line mura detection using machine learning and subspace method in display manufacturing

2022

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This paper discusses the automatic detection of mura, non-uniformity of brightness or color, which has been a long-standing challenge in the display industries. Our purpose is to develop a method using machine learning, which automatically detects and classifies mura in the front-end process. This will enable prompt feedback to the manufacturing process and contribute to improvement of the productivity. We propose "Progressive Hybrid model," which is based on the human visual perception and consists of a multiclass CNN (Convolutional Neural Network), a 2-class residual neural network, and a 2-class CNN. The two 2-class models based on the subspace method to reproduce the boundary-samples in the human visible test are for accurate classification between Normal displays and weak mura. To reproduce the appropriate

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“…Several schemes have been designed for backplane testing. Commonly used noncontact schemes are voltage imaging [14], [15], [16], optical charge sensing [17] and electron beam testing [18]. The common contact based techniques are charge feedthrough readout [19], transfer admittance technique [20] and the modified built in driver technique [11].…”

Section: Introductionmentioning

confidence: 99%

Test System for Thin Film Transistor Parameter Extraction in Active Matrix Backplanes

Daniel

Nair

Sambandan

2019

IEEE J. Electron Devices Soc.

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Thin film transistor (TFT) active matrix backplanes are used in large area electronic systems, such as displays and image sensors. With backplanes being fabricated on wearable and flexible substrates, the possibilities of operational faults in backplanes have increased. These faults could either be hard faults, such as line opens or shorts or could be softer faults, such as time dependent variations in the TFT transfer characteristics. Real time diagnosis of these faults require built-in-self-test systems. While many such systems have been demonstrated to diagnose hard faults, an easily realizable system to identify soft faults, such as variations in transistor transconductance remain an open challenge. In this paper, we discuss a system that extracts the transconductance by charging and then discharging the pixel capacitor at various gate voltages for an active matrix liquid crystal display backplane. This permits a plot of the time averaged current versus the gate voltage from which the spatial variation of transconductance can be extracted. The details of the design are discussed and a proof of concept with a 3 × 4 amorphous silicon backplane is demonstrated.

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Electron-beam testing of flat panel display substrates

Cited by 9 publications

References 1 publication

[Paper] High-Resolution Defect Detection for Flat Panel Display Using Proximity Capacitance Image Sensor

[Paper] High-Resolution Defect Detection for Flat Panel Display Using Proximity Capacitance Image Sensor

In‐line mura detection using machine learning and subspace method in display manufacturing

Test System for Thin Film Transistor Parameter Extraction in Active Matrix Backplanes

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