From Clues to Causes: Teacher-Guided Reasoning and Analysis through Cascaded Evidence for Wafer Defect Map Diagnosis

A complete wafer defect diagnosis should answer five questions in sequence: where the defect is, what it looks like, what type it is, why it occurred, and how it can be prevented. Existing wafer defect analysis methods mainly focus on defect localization, pattern recognition, and type classification, while root-cause diagnosis and actionable prevention remain largely unexplored. This leaves a substantial gap between visual defect recognition and process-level decision support. We propose TRACE, Teacher-guided Reasoning and Analysis through Cascaded Evidence, a reinforcement-learning framework that formulates wafer defect diagnosis as a five-stage reasoning process spanning defect position, appearance, type, root cause, and prevention. TRACE verifies the five diagnostic fields sequentially and halts at the first failed stage, preventing downstream rewards from being assigned on top of an already incorrect premise. A frozen snapshot of the policy holding the oracle then provides two forms of stage-specific guidance, neither of which directly reveals the answer. The first is a natural-language clue that constrains the reasoning space, while the second is a dense guidance signal derived from the centered difference between clue-conditioned and unconditioned logits of the same model. We show that this difference corresponds to a per-token pointwise mutual-information signal with the withheld clue information, and use it only to rescale the projection of the model’s own verifier-driven policy gradient. Because this rescaling acts as a positive-definite preconditioner rather than an auxiliary loss, it leaves the token-level stationary points of the original reinforcement-learning objective unchanged. No teacher, oracle, or clue is required to produce a diagnosis at inference time. We evaluate TRACE on a real-world industry wafer defect dataset using complete diagnostic-chain accuracy and counterfactual root-cause reasoning, where visually similar defect patterns arise from different manufacturing causes. The results demonstrate that TRACE improves not only defect recognition but also end-to-end diagnostic reasoning from visual evidence to root cause and prevention.