Inverse design of organic light-emitting diode structure based on deep neural networks
The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially with the number of design parameters, making it challenging to optimize the optica...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2021-11-01
|
| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2021-0434 |
| _version_ | 1811190864381214720 |
|---|---|
| author | Kim Sanmun Shin Jeong Min Lee Jaeho Park Chanhyung Lee Songju Park Juho Seo Dongjin Park Sehong Park Chan Y. Jang Min Seok |
| author_facet | Kim Sanmun Shin Jeong Min Lee Jaeho Park Chanhyung Lee Songju Park Juho Seo Dongjin Park Sehong Park Chan Y. Jang Min Seok |
| author_sort | Kim Sanmun |
| collection | DOAJ |
| description | The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially with the number of design parameters, making it challenging to optimize the optical properties of multilayer LEDs with rigorous electromagnetic simulations. In this work, we demonstrate an artificial neural network that can predict the light extraction efficiency of an organic LED structure in 30 ms, which is ∼103 times faster than the rigorous simulation in a single-treaded execution with root-mean-squared error of 1.86 × 10−3. The effective inference time per structure is brought down to ∼0.6 μs with unaltered error rate with parallelization. We also show that our neural networks can efficiently solve the inverse problem – finding a device design that exhibits the desired light extraction spectrum – within the similar time scale. We investigate the one-to-many mapping issue of the inverse problem and find that the degeneracy can be lifted by incorporating additional emission spectra at different observing angles. Furthermore, the forward neural network is combined with a conventional genetic algorithm to address additional large-scale optimization problems including maximization of light extraction efficiency and minimization of angle dependent color shift. Our approach establishes a platform for tackling computation-heavy optimization tasks with one-time computational cost. |
| first_indexed | 2024-04-11T14:57:33Z |
| format | Article |
| id | doaj.art-bc049cbc8f49424ab8dead14590449dc |
| institution | Directory Open Access Journal |
| issn | 2192-8614 |
| language | English |
| last_indexed | 2024-04-11T14:57:33Z |
| publishDate | 2021-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj.art-bc049cbc8f49424ab8dead14590449dc2022-12-22T04:17:11ZengDe GruyterNanophotonics2192-86142021-11-0110184533454110.1515/nanoph-2021-0434Inverse design of organic light-emitting diode structure based on deep neural networksKim Sanmun0Shin Jeong Min1Lee Jaeho2Park Chanhyung3Lee Songju4Park Juho5Seo Dongjin6Park Sehong7Park Chan Y.8Jang Min Seok9School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea OC Optical Technology Task, LG Display, Seoul, 07796, Republic of KoreaKC Machine Learning Lab, Seoul06181, Republic of KoreaSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially with the number of design parameters, making it challenging to optimize the optical properties of multilayer LEDs with rigorous electromagnetic simulations. In this work, we demonstrate an artificial neural network that can predict the light extraction efficiency of an organic LED structure in 30 ms, which is ∼103 times faster than the rigorous simulation in a single-treaded execution with root-mean-squared error of 1.86 × 10−3. The effective inference time per structure is brought down to ∼0.6 μs with unaltered error rate with parallelization. We also show that our neural networks can efficiently solve the inverse problem – finding a device design that exhibits the desired light extraction spectrum – within the similar time scale. We investigate the one-to-many mapping issue of the inverse problem and find that the degeneracy can be lifted by incorporating additional emission spectra at different observing angles. Furthermore, the forward neural network is combined with a conventional genetic algorithm to address additional large-scale optimization problems including maximization of light extraction efficiency and minimization of angle dependent color shift. Our approach establishes a platform for tackling computation-heavy optimization tasks with one-time computational cost.https://doi.org/10.1515/nanoph-2021-0434deep neural networkgenetic algorithminverse designlight extraction efficiencyorganic light-emitting diodes |
| spellingShingle | Kim Sanmun Shin Jeong Min Lee Jaeho Park Chanhyung Lee Songju Park Juho Seo Dongjin Park Sehong Park Chan Y. Jang Min Seok Inverse design of organic light-emitting diode structure based on deep neural networks Nanophotonics deep neural network genetic algorithm inverse design light extraction efficiency organic light-emitting diodes |
| title | Inverse design of organic light-emitting diode structure based on deep neural networks |
| title_full | Inverse design of organic light-emitting diode structure based on deep neural networks |
| title_fullStr | Inverse design of organic light-emitting diode structure based on deep neural networks |
| title_full_unstemmed | Inverse design of organic light-emitting diode structure based on deep neural networks |
| title_short | Inverse design of organic light-emitting diode structure based on deep neural networks |
| title_sort | inverse design of organic light emitting diode structure based on deep neural networks |
| topic | deep neural network genetic algorithm inverse design light extraction efficiency organic light-emitting diodes |
| url | https://doi.org/10.1515/nanoph-2021-0434 |
| work_keys_str_mv | AT kimsanmun inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT shinjeongmin inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT leejaeho inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT parkchanhyung inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT leesongju inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT parkjuho inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT seodongjin inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT parksehong inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT parkchany inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks AT jangminseok inversedesignoforganiclightemittingdiodestructurebasedondeepneuralnetworks |