A Bayesian Network Framework for Cross-Technology Knowledge Transfer Between Heterogeneous Solar Cell Manufacturing Platforms

Abstract

Engineering organizations transitioning between solar cell technology platforms (PERC to TOPCon, TOPCon to HJT, or any cross-platform transfer) face the challenge of preserving the latent knowledge embedded in the prior platform's troubleshooting history while adapting to the new platform's differing failure modes. We propose a Bayesian network framework that encodes the conditional dependencies among process variables, observable defect signatures, and root-cause categories in the source platform, and then performs structured prior-update on the target platform during ramp-up. The framework is benchmarked against simulated cross-platform transitions and demonstrates accelerated cause-attribution speed relative to platform-naive analysis. We discuss the prerequisites in terms of historical data quality and propose a maturity rubric for organizations contemplating formal cross-platform knowledge transfer programs.