Assisted Diagnostics Methodology for Complex High-Tech Applications

Controlling the operations and resolving product performance issues in today's high-tech production systems, such as semiconductor fabs, becomes a cumbersome task, even for experienced field engineers. To address the pressing need for assisted diagnostics approaches, in this paper we propose a model-based step-wise methodology, based on domain-specific languages and Bayesian networks, to capture domain knowledge and allow automated and guided reasoning in complex end-to-end diagnostics flow. We illustrate the methodology components and show its applied strength in a real industrial setting of semiconductor production chains.