Defect-Origin Root Cause Analysis Methodologies for Multi-Step Photovoltaic Cell Manufacturing Lines: A Comparative Review

Abstract

Root cause analysis (RCA) for end-of-line defects in multi-step photovoltaic cell manufacturing draws on a heterogeneous toolkit including five-why analysis, fault-tree analysis, fishbone diagrams, machine-learning attribution methods, and structured statistical screening. This review compares these methodologies on the dimensions of attribution accuracy, time-to-resolution, prerequisite data infrastructure, organizational adoption cost, and explainability. We map each methodology to defect-class categories � sensitive to upstream wafer quality, sensitive to process parameter drift, or sensitive to equipment-specific malfunctions � and recommend a triage framework that selects methodology based on defect signature. The review draws on 87 published RCA case studies from photovoltaic and adjacent industries published between 2010 and 2023 and identifies open methodological gaps suitable for future research.