Big data analytics have been employed to extract useful information and derive effective manufacturing intelligence for yield management in semiconductor manufacturing that is one of the most complex manufacturing processes due to tightly constrained production processes, reentrant process flows, sophisticated equipment, volatile demands, and complicated product mix. Indeed, the increasing adoption of multimode sensors, intelligent equipment, and robotics have enabled the Internet of Things (IOT) and big data analytics for semiconductor manufacturing. Although the processing tool, chamber set, and recipe are selected according to product design and previous experiences, domain knowledge has become less efficient for defect diagnosis and fault detection. To fill the gaps, this study aims to develop a framework based on Bayesian inference and Gibbs sampling to investigate the intricate semiconductor manufacturing data for fault detection to empower intelligent manufacturing. In addition, Cohen’s kappa coefficient was used to eliminate the influence of extraneous variables. The proposed approach was validated through an empirical study and simulation. The results have shown the practical viability of the proposed approach.