4.7 Article

In-situ spatial and temporal electrical characterization of ZnO thin films deposited by atmospheric pressure chemical vapour deposition on flexible polymer substrates

期刊

SCIENTIFIC REPORTS
卷 10, 期 1, 页码 -

出版社

NATURE RESEARCH
DOI: 10.1038/s41598-020-76993-4

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资金

  1. Natural Sciences and Engineering Research Council's Discovery program [RGPIN-2017-04212, RGPAS-2017-507977]
  2. Natural Sciences and Engineering Research Council's ENGAGE program
  3. Canadian Foundation for Innovation's John R. Evans Leaders Fund [35552]
  4. Ontario Research Fund-Research Infrastructure [35552]
  5. WIN Nanofellowship program

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A technique is presented for collecting data on both the spatial and temporal variations in the electrical properties of a film as it is deposited on a flexible substrate. A flexible printed circuit board substrate with parallel electrodes distributed across its surface was designed. Zinc oxide films were then deposited on the flexible substrate at different temperatures via atmospheric pressure chemical vapour deposition (AP-CVD) using a spatial atomic layer deposition system. AP-CVD is a promising high-throughput thin film deposition technique with applications in flexible electronics. Collecting data on the film properties in-situ allows us to directly observe how deposition conditions affect the evolution of those properties in real-time. The spatial uniformity of the growing film was monitored, and the various stages of film nucleation and growth on the polymer substrate were observed. The measured resistance of the films was observed to be very high until a critical amount of material has been deposited, consistent with Volmer-Weber growth. Furthermore, monitoring the film resistance during post-deposition cooling enabled immediate identification of metallic or semiconducting behaviour within the conductive ZnO films. This technique allows for a more complete understanding of metal chalcogen film growth and properties, and the high volume of data generated will be useful for future implementations of machine-learning directed materials science.

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