Modeling of ferroelectric hysteresis area of hard lead zirconate titanate ceramics: Artificial Neural Network approach

• Main Authors: W. Laosiritaworn, A. Ngamjarurojana, R. Yimnirun, Y. Laosiritaworn
• Format: Journal
• Published: 2018
• Subjects: Materials Science; Physics and Astronomy
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955697215&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/50943
• Institution: Chiang Mai University

In this work, the relationship between hysteresis area of hard lead zirconate titanate and external perturbation was modeled using the Artificial Neural Network (ANN). The model developed has the applied electric field parameters and temperature as inputs, and the hysteresis area as an output. Then ANN was trained with experimental data and used to predict hysteresis area of the unseen testing patterns of input. The predicted and the actual data of the testing set were found to agree very well for all considered input parameters. Furthermore, unlike previous power-law investigation where the low-field data had to be discarded in avoiding non-convergence problem, this work can model the data for the whole range with fine accuracy. This therefore suggests the ANN success in modeling hard ferroelectric hysteresis properties and underlines its superior performance upon typical power-law scaling technique. © Taylor & Francis Group, LLC.