Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-27T01:52:24.123Z Has data issue: false hasContentIssue false

Statistical Method to Optimize the Efficiency of Multi-Layer Polymer LEDs

Published online by Cambridge University Press:  21 March 2011

Michele Cecchi
Affiliation:
Cal Poly State University, Materials Engineering Dept, San Luis Obispo, CA
David Braun
Affiliation:
Cal Poly State University, Electrical Engineering Dept, San Luis Obispo, CA
Heather Smith
Affiliation:
Cal Poly State University, Statistics Dept, San Luis Obispo, CA
Linda Vanasupa
Affiliation:
Cal Poly State University, Materials Engineering Dept, San Luis Obispo, CA
Get access

Abstract

Research and development of displays and image sensors based on semiconducting polymers require design of new polymer materials and evaluation of film properties. Optimizing device performance using a one-factor-at-a-time (OFAT) requires screening the effect of several process parameters, running numerous samples, and may consume more scarce new material than desired. This paper investigates the effects of Indium-tin oxide (ITO), alkoxy-poly(pphenylene vinylene) (OC1C10-PPV) and poly(3,4-ethylene dioxythiophene) (PEDOT) layer preparation on polymer LED brightness and power efficiency by performing and analyzing a two-cubed full-factorial design experiment with 3 replicated center points. Full-factorial design evaluates all main factors and all interactions. Design of experiments (DOE) showed that correct selection of ITO anneal temperature can significantly improve brightness. Atomic Force Microscopy (AFM) measurements affirm that the increased brightness correlates with a reduction in ITO average surface roughness.

Type
Research Article
Copyright
Copyright © Materials Research Society 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Tang, C.W. and VanSlyke, S.A., Appl. Phys. Lett. 51, 913 (1987).Google Scholar
2. Park, Y., Choong, V., Gao, Y., Hsieh, B. R. and Tang, C. W., Appl. Phys. Lett. 68, 2699 (1996).Google Scholar
3. Brown, T.M., Kim, J.S., Friend, R.H., Cacialli, F., Daik, R. and Feast, W.J., Synthetic Metals 111–112 287 (2000).Google Scholar
4. Mason, M.G., Hung, L.S., Tang, C.W., Lee, S.T., Wong, K.W., and Wang, M., J. Appl. Phys. 86, 1688 (1999).Google Scholar
5. http://www.hpc.susx.ac.uk/∼venables/proj/metal1a.html#tableGoogle Scholar
6. Braun, D. and Heeger, A. J., Appl. Phys. Lett. 58, 1982 (1991); G. Gustafsson, Y. Cao, G. M. Treacy, F. Klavetter, N. Colaneri and A. J. Heeger, Nature 357 477 (1992).Google Scholar
7. Devore, Jay, Farnum, Nicholas, Applied Statistics for Engineers and Scientists, Duxbury Press, Pacific Grove 1999, p. 331.Google Scholar