Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In
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 a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/3182 |
| _version_ | 1797535353835356160 |
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| author | Seong-Chel Park Kwan-Ho Park Joon-Hyuk Chang |
| author_facet | Seong-Chel Park Kwan-Ho Park Joon-Hyuk Chang |
| author_sort | Seong-Chel Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:43:16Z |
| format | Article |
| id | doaj.art-4d0e88b4dbb54f31b3272abbab359f49 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:43:16Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4d0e88b4dbb54f31b3272abbab359f492023-11-21T18:16:43ZengMDPI AGSensors1424-82202021-05-01219318210.3390/s21093182Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-InSeong-Chel Park0Kwan-Ho Park1Joon-Hyuk Chang2Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Electronics and Computer Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Electronics and Computer Engineering, Hanyang University, Seoul 04763, KoreaWe 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.https://www.mdpi.com/1424-8220/21/9/3182thin-film transistor (TFT)organic light-emitting diode (OLED)compensation circuitluminance degradationartificial intelligencedeep neural network |
| spellingShingle | Seong-Chel Park Kwan-Ho Park Joon-Hyuk Chang Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In Sensors thin-film transistor (TFT) organic light-emitting diode (OLED) compensation circuit luminance degradation artificial intelligence deep neural network |
| title | Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In |
| title_full | Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In |
| title_fullStr | Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In |
| title_full_unstemmed | Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In |
| title_short | Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In |
| title_sort | luminance degradation compensation based on multistream self attention to address thin film transistor organic light emitting diode burn in |
| topic | thin-film transistor (TFT) organic light-emitting diode (OLED) compensation circuit luminance degradation artificial intelligence deep neural network |
| url | https://www.mdpi.com/1424-8220/21/9/3182 |
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