Title
Process optimization of gravure printed light-emitting polymer layers by a neural network approach
Author
Michels, J.J.
de Winter, S.H.P.M.
Symonds, L.H.G.
TNO Industrie en Techniek
Publication year
2009
Abstract
We demonstrate that artificial neural network modeling is a viable tool to predict the processing dependence of gravure printed light-emitting polymer layers for flexible OLED lighting applications. The (local) thickness of gravure printed light-emitting polymer (LEP) layers was analyzed using microdensitometry, after which the data was used to train a multi-layer neural network using error back propagation. Cell engraving depth, printing speed, and polymer concentration were used as input parameters of the neural network. Mean printed layer thickness, relative RMS roughness and feature anisotropy were defined as output parameters. The inhomogeneity of the gravure printed LEP layers was defined by two parameters, being the normalized standard deviation from the mean layer thickness, as well as the anisotropy or 'directionality' of the roughness features. Despite the limited number of input parameters, a fair prediction accuracy was obtained once new input data was fed into the trained network. The prediction error for the three output parameters was of the order: anisotropy > roughness > mean layer thickness. Calculating the magnitude of the output parameters as a function of the total space determined by the input parameters can be used as a way to find optimal printing conditions. These 'landscape' plots also reveal qualitative information on the rheological behavior of the inks during the printing process. © 2009 Elsevier B.V. All rights reserved.
Subject
High Tech Systems & Materials
Electronics
Industrial Innovation
Lighting
Neural networks
Organic electronics
Printing
Artificial neural network modeling
Error back propagation
Inhomogeneities
Input datas
Input parameter
Layer thickness
Light emitting polymer
Light-emitting polymer layers
Lighting applications
Optimal printing
Organic electronics
Output parameters
Polymer concentrations
Prediction accuracy
Prediction errors
Printing process
Printing speed
Process optimization
Qualitative information
Rheological behaviors
RMS roughness
Standard deviation
Two parameter
Anisotropy
Backpropagation
Graphic methods
Light emission
Lighting
Optimization
Organic light emitting diodes (OLED)
Polymers
Printing
Rheology
Neural networks
To reference this document use:
http://resolver.tudelft.nl/uuid:1cdcd27f-6001-435e-9e34-65a7418a2c1f
DOI
https://doi.org/10.1016/j.orgel.2009.08.015
TNO identifier
461624
ISSN
1566-1199
Source
Organic Electronics: physics, materials, applications, 10 (8), 1495-1504
Document type
article