Prediction of the electrical response of solution-processed thin-film transistors using multifactorial analysis

• Autor(a) principal: Braga, João P. [UNESP]
• Data de Publicação: 2019
• Outros Autores: Moises, Lucas A. [UNESP], Gozzi, Giovani [UNESP], Fugikawa-Santos, Lucas [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s10854-019-01695-1; http://hdl.handle.net/11449/189362

Thin-film transistors (TFTs) with the active layer composed by zinc oxide (ZnO) deposited via spray-pyrolysis present several advantages such as high electrical performance, high optical transmittance in the visible spectrum, low production cost and the ability to cover large areas. Besides the traditional application in electronic/optoelectronic circuits, ZnO TFTs can also be used in sensing devices due to its responsivity to UV-light. In the present work, we performed a bi-level full multifactorial analysis of TFT performance parameters exposed to UV-light. Characterization conditions like UV-light irradiance and time after UV exposure, as well as processing parameters such as annealing temperature were varied simultaneously, allowing the application of analysis of variance (ANOVA) to investigate the effect of these factors on the electrical performance of the devices. Field-effect mobility, threshold voltage, on/off current ratio and the device intrinsic current were among the parameters used as the responses in the factorial analysis. ANOVA was used to determine the ranking of significance of each factor on the different response parameters by the evaluation of the factor effects. Moreover, the results from ANOVA permitted the construction of linear functions used to predict the device responses in the whole range of the experimental conditions, which were confirmed by independent experimental results. The influence of factor interactions and of the linearization of some response parameters was also studied to improve the accuracy of TFT response prediction.