Predictive Loss-Analysis Frameworks for Identifying Yield-Limiting Factors During Solar Cell Production Ramp-Up

Abstract

During the ramp-up phase of a new solar cell production line, identifying which of dozens of candidate factors actually limits yield is the central engineering question. We describe a predictive loss-analysis framework that combines wafer-genealogy traceability, structured equation models linking process-step contributions to end-of-line cell efficiency, and Bayesian credibility intervals on factor-attributed losses. The framework accommodates the realities of ramp-up data: small-sample regimes, frequent recipe changes, and rapidly shifting process distributions. We illustrate the approach with two anonymized industrial ramp-up case studies and contrast the framework with conventional Pareto-of-defects, fishbone, and design-of-experiments analyses. Practical guidance is given for assembling the prerequisite traceability infrastructure and for staging the framework's deployment from first-silicon through steady-state production.