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Open AccessCommunication

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

Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Gianni D’Angelo
Sensors 2021, 21(9), 3182; https://doi.org/10.3390/s21093182
Received: 7 April 2021 / Revised: 30 April 2021 / Accepted: 1 May 2021 / Published: 3 May 2021
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. View Full-Text
Keywords: thin-film transistor (TFT); organic light-emitting diode (OLED); compensation circuit; luminance degradation; artificial intelligence; deep neural network; convolutional neural networks thin-film transistor (TFT); organic light-emitting diode (OLED); compensation circuit; luminance degradation; artificial intelligence; deep neural network; convolutional neural networks
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MDPI and ACS Style

Park, S.-C.; Park, K.-H.; Chang, J.-H. Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In. Sensors 2021, 21, 3182. https://doi.org/10.3390/s21093182

AMA Style

Park S-C, Park K-H, Chang J-H. Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In. Sensors. 2021; 21(9):3182. https://doi.org/10.3390/s21093182

Chicago/Turabian Style

Park, Seong-Chel; Park, Kwan-Ho; Chang, Joon-Hyuk. 2021. "Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In" Sensors 21, no. 9: 3182. https://doi.org/10.3390/s21093182

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