Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization  

作　　者：Marius Singler Akshay Patil Linda Ney Andreas Lorenz Sebastian Tepner Florian Clement 

机构地区：[1]Fraunhofer Institute for Solar Energy Systems(ISE),Heidenhofstr.2,79110 Freiburg,Germany [2]EKRA Automatisierungstechnik GmbH,Zeppelinstraße 16,74357 Bonnigheim,Germany

出　　处：《Energy and AI》2024年第3期513-524,共12页能源与人工智能（英文）

基　　金：supported by the German Federal Ministry of Economic Affairs and Climate Protection(BMWK)within the funding program“Innovations for the Energy Transition”under the contract number 03EE1157D(Avatar).

摘　　要：The industrial metallization of Si solar cells predominantly relies on screen printing,with silver as the preferred electrode material.However,the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks,constrictions and spreading.Real-time monitoring of quality parameters during production has thus become increasingly critical.Current inline optical quality control systems usually only include 2D visualizations of the printed layout,which limits their effectiveness in quality control.Options that allow 3D measurements are usually slow,expensive,and therefore not worth considering in most cases.This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning.Furthermore,a workflow for the generation of training data,which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope(CLSM)to accurately represent solar cell fingers,is presented.After model training,the predicted height maps are compared with the ground truth height maps,and the robustness of the model with respect to a paste variation and screen parameter variation is examined.The results confirm the feasibility and reliability of deep learning-based 3D shape estimation,extending its applicability to new,previously unseen data from screen-printed contact fingers.With a structural similarity index(SSIM)score of 0.76,a strong correlation between the estimated and ground truth height maps is established.In summary,our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

关 键 词：Deep Learning Front-side metallization Image2Heigth Solar cell Screen-printing 

分 类 号：TM914.4[电气工程—电力电子与电力传动]