Temperature-time transformation diagram for Pb(Zr,Ti)O3 thin films

Ronnie Varghese; Matthew Williams; Shashaank Gupta; Shashank Priya

doi:10.1063/1.3606433

Temperature-time transformation diagram for Pb(Zr,Ti)O₃ thin films

Ronnie Varghese, Matthew Williams, Shashaank Gupta, Shashank Priya

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In this paper, we describe an analytical model to define the temperature-time- transformation (TTT) diagram of sol-gel deposited Pb(Zr,Ti)O₃ thin films on platinized silicon substrates. Texture evolution in film occurred as the pyrolysis and thermal annealing conditions were varied. We demonstrate that the developed model can quantitatively predict the outcome of thermal treatment conditions in terms of texture evolution. Multinomial and multivariate regression techniques were utilized to create the predictor models for TTT data. Further, it was found that multinomial regression can provide better fit as compared to standard regression and multivariate regression. We have generalized this approach so that it can be applied to other thin film deposition techniques and bulk ceramics.

Original language	English (US)
Article number	014109
Journal	Journal of Applied Physics
Volume	110
Issue number	1
DOIs	https://doi.org/10.1063/1.3606433
State	Published - Jul 1 2011
Externally published	Yes

Bibliographical note

Funding Information:
The authors gratefully acknowledge financial support from the Air Force Office of Scientific Research (AFOSR), and Institute for Critical Technologies and Applied Science (ICTAS).

Publisher link

10.1063/1.3606433

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title = "Temperature-time transformation diagram for Pb(Zr,Ti)O3 thin films",

abstract = "In this paper, we describe an analytical model to define the temperature-time- transformation (TTT) diagram of sol-gel deposited Pb(Zr,Ti)O3 thin films on platinized silicon substrates. Texture evolution in film occurred as the pyrolysis and thermal annealing conditions were varied. We demonstrate that the developed model can quantitatively predict the outcome of thermal treatment conditions in terms of texture evolution. Multinomial and multivariate regression techniques were utilized to create the predictor models for TTT data. Further, it was found that multinomial regression can provide better fit as compared to standard regression and multivariate regression. We have generalized this approach so that it can be applied to other thin film deposition techniques and bulk ceramics.",

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AU - Priya, Shashank

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