Research area

RESEARCH INTERESTS

Theoretical analysis and demonstration of applied electronics based on nano photonics

Research interests are focused on nanophotonics for the study of novel nanophotonic physics, as well as application of such nanostructures for light emitting & photo-voltaic devices, flexible and transparent displays, and active nanophotonic materials. Recently, we enlarge our research scope combining "Machine Learning".

• Nano-optics, Nano-photonics, Surface plasmon fundamentals
• Nano optical phenomena and their applications for industrial use (Displays, PVs)
• Components of transparent/Flexible displays and electronics with nano structures
• Machine Learning based design methods for nano-photonic devices
• Other applications (Light therapy, Bio/Health, Sensors, and etc.)

Toward next generation displays and photonic devices

1) Structural color technology for imaging devices

Color is one of the most important component to represent electrical information more realistic. LCDs(Liquid crystal displays) generate three basis colors in which white light from backlight unit passes through the color filter divided into three subpixels (Red/Green/Blue). Even light emitting devices like OLEDs, micro-LEDs, quantom dots utilize some auxiliary method to enhance the color purity. Imaging sensors in digital camera also requires color filter to collect full color data on pictures.

Interestingly, 'Nano structures' can represent color without any coloring materials (such as pigments or dyes). Compared to the material based-colors, structural color shows better stability from heat, light, and chemicals. In addition, the design method is quite simple. There needs more progress for practical industrial use as follows.

- Coloring structure with plasmonic and diffraction optics
- Angle invariant color
- Flexible / Stretchable structural color
- Super high resolved pixels and its optical issues

2) Super high resolved 'Displays and Image sensors'

High resolution leads to clearer information being provided to users. Images in displays or image sensors are replicated in greater detail as pixel size decreases and as the number of pixels become larger. However, the reduced pixel size under a few microns can cause diffraction as well as can be hardly formed from conventional fabrication process for patterning. Diffraction phenomenon in the tiny aperture may cause diverging of the light propagation path. It consequently cause the crosstalk between pixels which means noise signal to adjacent pixels.
On the other hand, various nano photonic phenomena handle the lights with complex interactions. For example, surface plasmons enhance and trap EM waves near the interfaces, thus it can control the diffractive wave to have other characteristics. In this point of view, nano-optical phenomenon or structural color can be good alternatives to the super high resolved pixels with color filters. Our research group are focus not only on analysis on nano photonic phenomenon, but also the possible application technologies of nano photonics.

a) Polymer opals Univ. of Cambridge (2013)
b) Nano drawing Singapore University of Technology and Design (2014)
c) Non-iridescent color film Seoul National University (2012)
d) Photonic pigment Harvard University (2014)

a) Conventional color filter (color resist)
b) Plasmonic color filter design
c) Quantitative analysis of the cross-talk issue between adjacent pixels

3) High efficient and High color purity self-emissive devices

Self emissive technologies, such as OLEDs, micro-LEDs, and QLEDs, emit light without the need for an external light source. In general, they are made up of multi-layered thin films of organic or inorganic materials sandwiched between two electrodes and are used in a variety of applications such as lighting, displays, and sensors. To improve the light extraction efficiency and color purity of these devices, various microcavity structures have been investigated. The light intensity and purity of the RGB colors can be increased by the strong resonance effect formed between the two electrodes. Further research is required to realize the ultimate optical uses of the future, as follows.

- Improved light extraction efficiency and color purity than conventional self-emissive devices
- Fabrication method without micro patterning process such as fine metal mask (FMM)- Display panel structure without color filter with pigment and dye materials
- Angular independence microcavity structure
- Transparent, flexible, or wearable self-emissive devices-

<Example of high color purity OLED>
- Demonstration / Device analysis on Optical and Color performances

Multi-functional photo-voltaic devices

Optical & opto-electrical design for photovoltaics / Colored or Transparent PVs

A solar cell is a device that converts light into electricity. Solar cells with various absorber layers, such as silicon, compound semiconductors, and organic materials, are available, and numerous studies are being conducted to achieve high power conversion efficiency at a low cost. For example, if the thickness of the absorber layer is reduced to save money, the efficiency of the solar cell suffers as a result.

Internal light reflection can be used to increase the efficiency of a solar cell by sandwiching a passivation layer between the absorb layer and the interface.

Using photoelectric modeling to adjust the passivation layer's local contact opening width and pitch, our group is investigating the relationship between the passivation layer's performance and structure, as well as structural parameters on the nano-micro scale.

We may also propose a method for fabricating high-efficiency thin-film solar cells for PV generators and sensors, and we are developing optical and photovoltaic cells for color or transparent PV using transparent substrates and electrodes.

Machine learning based design method for photonic devices

Designing optical and photonic devices with neural network technologies

Nano resonance phenomena are determined by geometrical factors of their structures as well as the optical constants of the consisted materials. In general, the design and optimization of the nano-photonic structures are performed by iterative numerical simulations. The preciser step of the parameters is, the more accurate (or possibly the more optimized characteristics could be obtained. And it requires computational burden in both time and system. In order to reduce the calculating trials and time for designing, in-depth theoretical backgrounds and prior knowledge of the problems are required. With this approach, the amount of computation increases exponentially as the structure becomes more complex.

Recently, machine learning technologies have been changing this paradigm, especially the 'Deep learning'. Deep learning consisting of the multilayered artificial neural networks is one of major subset of the machine learning, and it provides high accuracy prediction or classification. Previously simulated dataset is used for training to match the structure parameters to the photonic characteristics.

Once trained, the network can works to design a photonic structure without any additional computational resources. We research the deep learning technology to the following opto- or photonic devices.

- Design of photonic structures
: Meta-materials, Plasmonics, Nano-resonators, etc..
- Photonic applications
: Structural colors, Holographic devices,
Light emitting devices with nano structures,
Photonic circuits such as power splitters & converters, etc...

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