RUS  ENG
Full version
JOURNALS // Pisma v Zhurnal Tekhnicheskoi Fiziki // Archive

Pisma v Zhurnal Tekhnicheskoi Fiziki, 2018 Volume 44, Issue 9, Pages 81–87 (Mi pjtf5822)

A neural-network method of predicting defect formation on the surface of thin ITO films under mechanical load

D. A. Kirienko, O. Ya. Berezina

Petrozavodsk State University

Abstract: A method for determining the number of defects arising under compressive and tensile stress in bended thin transparent conducting coatings on polymer substrates is proposed. This algorithm is based on the use of mathematical methods of artificial neural networks. The network is trained for calculating the average defect density per unit length at the input parameters corresponding to film and substrate sizes, surface resistance of the conducting coating, and bending radius. The application of this method allows one to determine the average defect density with high accuracy.

Received: 11.01.2018

DOI: 10.21883/PJTF.2018.09.46069.17207


 English version:
Technical Physics Letters, 2018, 44:5, 401–403

Bibliographic databases:


© Steklov Math. Inst. of RAS, 2026