Development in Nano-Arrays Using Deep Learning: A New AI Method that could Design Nanohole Arrays that can Produce a Specific Structural Color

The diversity in colors is much more and increases with the combination of two or more colors. Light interacts with tiny nanostructures and creates an intrinsic pattern of multiple colors. Light spectrum also interacts with the holes and creates a system called nano-hole arrays. This also can distinguish light phenomena and obtain structural colors. The main purpose is to implant structural colors in manmade materials. The main advantage of this color is that these colors won’t degrade over time. Researchers are still facing the problem to create a nanoscale array that results in a specified color. This falls under a broad category of Computer Vision.

A team of researchers from Chongqing University designed a new system that could enhance these nanohole arrays into structural colors. They also used various Machine Learning models in designing this system. To predict the structural colors of these arrays the researchers developed two Deep Learning models CSC and CSS. These models allowed the formation of the nanohole arrays, which resulted in the creation of the desired colors. Parameters like accuracy, F1 score, recall, precision, and percentage accuracy were quite remarkable. The team of researchers stated that the results were based on the simulation of these arrays. These results were transformed into the experimental reality and results were enhanced to a large extent.

These results were considered for further evaluation and parameters like accuracy and F1 score were obtained for the testing dataset. The prediction model was created to predict the data that was being enhanced via Deep Learning models used previously. The model also aims in bringing theoretical gaps between various applications and theory concepts. Nanohole arrays are also implemented for high-density storage consisting of diverse data.

The study has demonstrated a Deep Learning model for implementing the structure color and spectrum of the nano-arrays. The scalability of this method is promising as it can manage larger datasets. It can also implement complex structures which potentially adapt to different materials. This research will simply manipulate nano-arrays and their plasmic applications.

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