Abstract
Abstract 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.
Highlights
Organic light-emitting diodes (OLED) are widely used lightemitting diodes that utilize the light emission from an electronhole annihilation inside an organic material [1]
We show that an artificial neural network can be trained to solve the inverse problem instantly: finding an OLED structure exhibiting a given light extraction efficiency (LEE) spectrum
The details on how the neural network assisted genetic algorithm (NNGA) work can be found in Section S7 of the Supplementary Material, and a detailed discussion regarding the one-time computational cost of NNGA can be found in Section S5 of Supplementary Material
Summary
Organic light-emitting diodes (OLED) are widely used lightemitting diodes that utilize the light emission from an electronhole annihilation inside an organic material [1]. Multiple reflections and light interferences inside a layered OLED cause its optical characteristics to depend heavily on the device shape; even with the same materials, the light emission efficiencies and spectral characteristics can be dramatically varied [9, 10] It leads to an angular dependence of the emission spectra while narrowing down the emission spectra. We combine the forward network with a genetic algorithm to tackle additional OLED optimization problems including maximization of light extraction efficiency and minimization of angular color shift. The results of these large-scale optimization problems provide interesting physical insights that can be potentially generalized in designing OLED devices.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call