scholarly journals Uniform Pressing Mechanism in Large-Area Roll-to-Roll Nanoimprint Lithography Process

2021 ◽  
Vol 11 (20) ◽  
pp. 9571
Author(s):  
Ga Eul Kim ◽  
Hyuntae Kim ◽  
Kyoohee Woo ◽  
Yousung Kang ◽  
Seung-Hyun Lee ◽  
...  
Keyword(s):  
Contact Pressure ◽  
Nanoimprint Lithography ◽  
Applied Pressure ◽  
Machine Learning Techniques ◽  
Conventional System ◽  
Large Area ◽  
High Productivity ◽  
Roll To Roll ◽  
Learning Techniques ◽  
Roller Machine

We aimed to increase the processing area of the roll-to-roll (R2R) nanoimprint lithography (NIL) process for high productivity, using a long roller. It is common for a long roller to have bending deformation, geometric errors and misalignment. This causes the non-uniformity of contact pressure between the rollers, which leads to defects such as non-uniform patterning. The non-uniformity of the contact pressure of the conventional R2R NIL system was investigated through finite element (FE) analysis and experiments in the conventional system. To solve the problem, a new large-area R2R NIL uniform pressing system with five multi-backup rollers was proposed and manufactured instead of the conventional system. As a preliminary experiment, the possibility of uniform contact pressure was confirmed by using only the pressure at both ends and one backup roller in the center. A more even contact pressure was achieved by using all five backup rollers and applying an appropriate pushing force to each backup roller. Machine learning techniques were applied to find the optimal combination of the pushing forces. In the conventional pressing process, it was confirmed that pressure deviation of the contact area occurred at a level of 44%; when the improved system was applied, pressure deviation dropped to 5%.

scholarly journals UV-nanoimprint lithography and large area roll-to-roll texturization with hyperbranched polymer nanocomposites for light-trapping applications

2012 ◽  
Vol 103 ◽  
pp. 147-156 ◽  
Author(s):  
Marina A. González Lazo ◽  
Rémy Teuscher ◽  
Yves Leterrier ◽  
Jan-Anders E. Månson ◽  
Caroline Calderone ◽  
...  
Keyword(s):  
Polymer Nanocomposites ◽  
Nanoimprint Lithography ◽  
Hyperbranched Polymer ◽  
Light Trapping ◽  
Large Area ◽  
Roll To Roll

scholarly journals Knowledge Discovery Workflows in the Exploration of Complex Astronomical Datasets

2012 ◽  
Vol 10 (H16) ◽  
pp. 681-682
Author(s):  
Raffaele D'Abrusco ◽  
Giuseppina Fabbiano ◽  
Omar Laurino ◽  
Francesco Massaro
Keyword(s):  
Knowledge Discovery ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Large Area ◽  
Learning Techniques ◽  
Inadequate Knowledge ◽  
Powerful Approach ◽  
Multi Wavelength ◽  
High Dimensional Datasets

AbstractThe massive amount of data produced by the recent multi-wavelength large-area surveys has spurred the growth of unprecedentedly massive and complex astronomical datasets that are proving the traditional data analysis techniques more and more inadequate. Knowledge discovery techniques, while relatively new to astronomy, have been successfully applied in several other quantitative disciplines for the determination of patterns in extremely complex datasets. The concerted use of different unsupervised and supervised machine learning techniques, in particular, can be a powerful approach to answer specific questions involving high-dimensional datasets and degenerate observables. In this paper I will present CLaSPS, a data-driven methodology for the discovery of patterns in high-dimensional astronomical datasets based on the combination of clustering techniques and pattern recognition algorithms. I shall also describe the result of the application of CLaSPS to a sample of a peculiar class of AGNs, the blazars.

Inkjetable and photo-curable resists for large-area and high-throughput roll-to-roll nanoimprint lithography

2014 ◽  
Vol 13 (4) ◽  
pp. 043003 ◽  
Author(s):  
Manuel W. Thesen ◽  
Dieter Nees ◽  
Stephan Ruttloff ◽  
Maximilian Rumler ◽  
Mathias Rommel ◽  
...  
Keyword(s):  
High Throughput ◽  
Nanoimprint Lithography ◽  
Large Area ◽  
Roll To Roll

Roll-to-roll nanoimprint lithography for patterning on a large-area substrate roll

2014 ◽  
Vol 123 ◽  
pp. 18-22 ◽  
Author(s):  
Hyungjun Lim ◽  
Kee-bong Choi ◽  
Geehong Kim ◽  
Sunghwi Lee ◽  
Hyunha Park ◽  
...  
Keyword(s):  
Nanoimprint Lithography ◽  
Large Area ◽  
Roll To Roll

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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