Identification the Role of Grain Boundaries in Polycrystalline Photovoltaics via Advanced Atomic Force Microscope

Atomicforce microscopy (AFM)-based scanning probing techniques, including Kelvinprobe force microscopy (KPFM) and conductive atomic force microscopy (C-AFM), have been widely applied to investigate thelocal electromagnetic, physical, or molecular characteristics of functional materials on a microscopic scale. The microscopic inhomogeneities of the electronic properties of polycrystalline photovoltaic materials can be examined by these advanced AFM techniques, which bridge the local properties of materials to overall device performance and guide the optimization of the photovoltaic devices. In this review, the critical roles of local optoelectronic heterogeneities, especially at grain interiors (GIs) and grain boundaries (GBs) of polycrystalline photovoltaic materials, including versatile polycrystalline silicon, inorganic compound materials, and emerging halide perovskites, studied by KPFM and C-AFM, are systematically identified. How the band alignment and electrical properties of GIs and GBs affect the carrier transport behavior are discussed from the respective of photovoltaic research. Further exploiting the potential of such AFM-based techniques upon a summary of their up-to-date applications in polycrystalline photovoltaic materials is beneficial to acomprehensive understanding of the design and manipulation principles of thenovel solar cells and facilitating the development of the next-generation photovoltaics and optoelectronics. The critical roles of local optoelectronic heterogeneities, especially at grain interiors (GIs) and grain boundaries (GBs) of polycrystalline photovoltaic materials, including polycrystalline silicon, inorganic compound materials, and emerging halide perovskites, can be identified by the advanced atomic force microscope (AFM)-based techniques, which bridge the local properties of materials to overall device performance and guide the optimization of the photovoltaic devices.image