No abstract

Keywords:Laser firing Laser processing Heterojunction Solar cell Surface treatment One of the key steps to achieve high efficiencies in amorphous/crystalline silicon photovoltaic structures is to design low-ohmic-resistance back contacts with good passivation in the rear part of the cell. A well known approach to achieve this goal is to use laser-flred contact (LFC) processes in which a metal layer is fired through the dielectric to define good contacts with the semiconductor. However, and despite the fact that this approach has demonstrated to be extremely successful, there is still enough room for process improvement with an appropriate optimization. In this paper, a study focused on the optimal adjustment of the irradiation parameters to produce laser-fired contacts in a-Si:H/c-Si heterojunction solar cells is presented. We used samples consisting of crystalline-silicon (c-Si) wafers together with a passivation layer of intrinsic hydrogenated amorphous silicon (a-Si:H(i)) deposited by plasma-enhanced chemical deposition (PECVD). Then, an aluminum layer was evaporated on both sides, the thickness of this layer varied from 0.2 to 1 u,m in order to identify the optimal amount of Al required to create an appropriate contact. A q-switched NdiYVCu laser source, A. = 532 nm, was used to locally fire the aluminum through the thin a-Si:H(i)-layers to form the LFC. The effects of laser fluences were analyzed using a comprehensive morphological and electrical characterization.

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

Cracks are pathologies whose appearance in ceramic tiles can cause various types of scratches due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time to map the entire area and high cost. These inspections require special equipment when they are at high altitudes, and the integrity of the inspector is at risk. Thus, there exists a need for automated optical inspection to find faults in ceramic tiles. This work focuses on the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes a pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The results show that the proposed architecture for ceramic crack segmentation achieves promising performance