Degradation mechanisms in organic light-emitting diodes

Langlois, Eric; Wang, D.; Shen, Jun; Barrow, W. A.; Green, Patrick J.; Tang, Ching Wan; Shi, Jianmin

doi:10.1117/12.386370

Cited by 7 publications

(7 citation statements)

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“…(iv) Mobile Ionic Impurities Model. The degradation model based on the presence of mobile ionic impurities was introduced to explain a common observation that has almost always been associated with the decrease in device luminance during OLED operation, which is the tendency of the driving voltage to increase. − The increase in voltage is however often reversible, at least partially, on either resting the device at zero applied bias or reversing its polarity. , Shen and co-workers 88 presented a systematic study of this phenomenon on a standard bilayer AlQ 3 -based OLED. The device degradation, reflecting an operating voltage increase and electroluminescence decrease, was modeled by assuming the existence of mobile ions in the organic layers of the OLED.…”

Section: Intrinsic Degradationmentioning

confidence: 99%

“…The experimental evidence for the existence of both reversible and irreversible components of device degradation is strong. Redistribution of mobile ions, − reorientation of internal dipoles, , or redistribution of space charge due to various trapping effects are all plausible candidates for explaining the reversible part of changes in device operation. It is therefore highly likely that device degradation is governed by different phenomena in different time scales.…”

Section: Intrinsic Degradationmentioning

confidence: 99%

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Degradation Phenomena in Small-Molecule Organic Light-Emitting Devices

2004

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Studies of electroluminescence degradation mechanisms in small-molecule-based organic light-emitting devices (OLEDs) are reviewed. Luminescence degradation due to the growth of visible nonemissive defects, widely referred to as "dark spots", as well as device catastrophic failure phenomena are addressed briefly. A special emphasis is given to intrinsic degradation phenomena that cause the decrease in the electroluminescence efficiency of the OLEDs during operation. In the discussion of intrinsic degradation, some widely accepted models that have been proposed to explain the degradation behavior are introduced and reviewed in view of experimental observations. These models are (i) the morphological instability model, (ii) the unstable cationic AlQ 3 model, (iii) the indium migration model, (iv) the mobile ionic impurities model, and (v) the immobile positive charge accumulation model.

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Section: Intrinsic Degradationmentioning

confidence: 99%

Section: Intrinsic Degradationmentioning

confidence: 99%

Degradation Phenomena in Small-Molecule Organic Light-Emitting Devices

2004

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“…Blue consumes more power than red and green components do [ 10 , 11 , 12 ]. Ultimately, the burn-in phenomenon is a major cause of image and video-quality deterioration over time [ 13 , 14 , 15 , 16 ]. Therefore, research on pixel compensation technology that effectively addresses the burn-in phenomenon of OLED displays is important to continuously provide high-quality images and videos to users.…”

Section: Introductionmentioning

confidence: 99%

Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In

Park

Chang

2021

Sensors

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We propose a deep-learning algorithm that directly compensates for luminance degradation because of the deterioration of organic light-emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuits are encumbered by high cost of the development and manufacturing processes because of their complexity. However, given that deep-learning algorithms are typically mounted onto systems on chip (SoC), the complexity of the circuit design is reduced, and the circuit can be reused by only relearning the changed characteristics of the new pixel device. The proposed approach comprises deep-feature generation and multistream self-attention, which decipher the importance of the variables, and the correlation between burn-in-related variables. It also utilizes a deep neural network that identifies the nonlinear relationship between extracted features and luminance degradation. Thereafter, luminance degradation is estimated from burn-in-related variables, and the burn-in phenomenon can be addressed by compensating for luminance degradation. Experiment results revealed that compensation was successfully achieved within an error range of 4.56%, and demonstrated the potential of a new approach that could mitigate the burn-in phenomenon by directly compensating for pixel-level luminance deviation.

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“…Eventually, as the usage time increases, the deterioration of the OLED device accelerates, and luminance degradation occurs [4,5]. Ultimately, the burn-in phenomenon is a major cause of the deterioration of the image and video quality over time [6][7][8][9]. Therefore, research on pixel compensation technology that effectively addresses the burn-in phenomenon of OLED displays is important to continuously provide high-quality images and videos to users.…”

Section: Introductionmentioning

confidence: 99%

Luminance Degradation Compensation Based on Multi-Stream Self-Attention to Address Thin Film Transistor-Organic Light Emitting Diode Burn-in

Park¹,

Park²,

Chang³

2021

Preprint

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In this study, we propose a deep learning algorithm that directly compensates for luminance degradation owing to the deterioration of organic light emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuits are encumbered by a high cost of development and manufacturing processes owing to their complexity. However, given that deep learning algorithms are typically mounted on a system on chip (SoC), the complexity of the circuit design is reduced, and the circuit can be reused by re-learning only the changed characteristics of the new pixel device. The proposed approach comprises deep feature generation and multi-stream self-attention, which decipher the importance of the variables, and the correlation between burn-in-related variables. It also utilizes a deep neural network that identifies the nonlinear relationship between the extracted features and luminance degradation. Thereafter, the luminance degradation is estimated from the burn-in-related variables, and the burn-in phenomenon can be addressed by compensating for the luminance degradation. The experimental results revealed that compensation was successfully achieved within an error range of 2.69%, and demonstrate the potential of a new approach that can mitigate the burn-in phenomenon by directly compensating for pixel-level luminance deviation.

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Degradation mechanisms in organic light-emitting diodes

Cited by 7 publications

References 0 publications

Degradation Phenomena in Small-Molecule Organic Light-Emitting Devices

Degradation Phenomena in Small-Molecule Organic Light-Emitting Devices

Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In

Luminance Degradation Compensation Based on Multi-Stream Self-Attention to Address Thin Film Transistor-Organic Light Emitting Diode Burn-in

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