Ultraviolet photodetectors have been widely utilized in several applications, such as advanced communication, ozone sensing, air purification, flame detection, etc. Gallium nitride and its compound semiconductors have been promising candidates in photodetection applications. Unlike polar gallium nitride-based optoelectronics, non-polar gallium nitride-based optoelectronics have gained huge attention due to the piezoelectric and spontaneous polarization effect–induced quantum confined-stark effect being eliminated. In turn, non-polar gallium nitride-based photodetectors portray higher efficiency and faster response compared to the polar growth direction. To date, however, a systematic literature review of non-polar gallium nitride-based photodetectors has yet to be demonstrated. Hence, the objective of this systematic literature review is to critically analyze the data related to non-polar gallium nitride-based photodetectors. Based on the pool of literature, three categories are introduced, namely, growth and fabrication, electrical properties, and structural, morphological, and optical properties. In addition, bibliometric analysis, a precise open-source tool, was used to conduct a comprehensive science mapping analysis of non-polar gallium nitride-based photodetectors. Finally, challenges, motivations, and future opportunities of non-polar gallium nitride-based photodetectors are presented. The future opportunities of non-polar GaN-based photodetectors in terms of growth conditions, fabrication, and characterization are also presented. This systematic literature review can provide initial reading material for researchers and industries working on non-polar gallium nitride-based photodetectors.
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In this search, we examined the factorial experiments and the study of the significance of the main effects, the interaction of the factors and their simple effects by the F test (ANOVA) for analyze the data of the factorial experience. It is also known that the analysis of variance requires several assumptions to achieve them, Therefore, in case of violation of one of these conditions we conduct a transform to the data in order to match or achieve the conditions of analysis of variance, but it was noted that these transfers do not produce accurate results, so we resort to tests or non-parametric methods that work as a solution or alternative to the parametric tests , these method
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